├── .gitignore ├── LICENSE ├── README.md ├── docs ├── 404.png ├── eyeballer_logo.png ├── homepage.png ├── login.png ├── old-looking.png └── parked.png ├── eyeballer.py ├── eyeballer ├── __init__.py ├── augmentation.py ├── model.py └── visualization.py ├── prediction_output_template.html ├── requirements-gpu.txt ├── requirements.txt ├── tests ├── PredictTest.py ├── __init__.py ├── data │ ├── 404.png │ ├── empty.png │ ├── homepage.png │ ├── invalid.png │ ├── login.png │ ├── nothing.png │ ├── odd_shape.png │ ├── old-looking.png │ └── small.png └── models │ └── test_weights.h5 └── utils ├── labelbox_to_labels.py ├── reroll.py ├── resizer.sh └── verify.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | 106 | #screenshot data 107 | *.png 108 | *.jpg 109 | *.tar 110 | 111 | #model files 112 | *.h5 113 | *.model 114 | *.csv 115 | *.txt 116 | results.html 117 | !requirements.txt 118 | !requirements-gpu.txt 119 | !tests/models/* 120 | !tests/data/* 121 | !docs/* 122 | logs/* 123 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Eyeballer 2 | 3 | ![Logo](/docs/eyeballer_logo.png) 4 | 5 | 6 | Give those screenshots of yours a quick eyeballing. 7 | 8 | Eyeballer is meant for large-scope network penetration tests where you need to find "interesting" targets from a huge set of web-based hosts. Go ahead and use your favorite screenshotting tool like normal (EyeWitness or GoWitness) and then run them through Eyeballer to tell you what's likely to contain vulnerabilities, and what isn't. 9 | 10 | Give it a try live at: https://eyeballer.bishopfox.com 11 | 12 | ### Example Labels 13 | 14 | | Old-Looking Sites | Login Pages | 15 | | ------ |:-----:| 16 | | ![Sample Old-looking Page](/docs/old-looking.png) | ![Sample Login Page](/docs/login.png) | 17 | 18 | | Webapp | Custom 404's | 19 | | ------ |:-----:| 20 | | ![Sample Webapp](/docs/homepage.png) | ![Sample Custom 404](/docs/404.png) | 21 | 22 | | Parked Domains | 23 | | ------ | 24 | | ![Sample Webapp](/docs/parked.png) | 25 | 26 | ## What the Labels Mean 27 | 28 | **Old-Looking Sites** 29 | Blocky frames, broken CSS, that certain "je ne sais quoi" of a website that looks like it was designed in the early 2000's. You know it when you see it. Old websites aren't just ugly, they're also typically super vulnerable. When you're looking to hack into something, these websites are a gold mine. 30 | 31 | **Login Pages** 32 | Login pages are valuable to pen testing, they indicate that there's additional functionality you don't currently have access to. It also means there's a simple follow-up process of credential enumeration attacks. You might think that you can set a simple heuristic to find login pages, but in practice it's really hard. Modern sites don't just use a simple input tag we can grep for. 33 | 34 | **Webapp** 35 | This tells you that there is a larger group of pages and functionality available here that can serve as surface area to attack. This is in contrast to a simple login page, with no other functionality. Or a default IIS landing page which has no other functionality. This label should indicate to you that there is a web application here to attack. 36 | 37 | **Custom 404** 38 | Modern sites love to have cutesy custom 404 pages with pictures of broken robots or sad looking dogs. Unfortunately, they also love to return HTTP 200 response codes while they do it. More often, the "404" page doesn't even contain the text "404" in it. These pages are typically uninteresting, despite having a lot going on visually, and Eyeballer can help you sift them out. 39 | 40 | **Parked Domains** 41 | Parked domains are websites that look real, but aren't valid attack surface. They're stand-in pages, usually devoid of any real functionality, consist almost entirely of ads, and are usually not run by our actual target. It's what you get when the domain specified is wrong or lapsed. Finding these pages and removing them from scope is really valuable over time. 42 | 43 | ## Setup 44 | 45 | Download required packages on pip: 46 | ``` 47 | sudo pip3 install -r requirements.txt 48 | ``` 49 | 50 | Or if you want GPU support: 51 | ``` 52 | sudo pip3 install -r requirements-gpu.txt 53 | ``` 54 | 55 | **NOTE**: Setting up a GPU for use with TensorFlow is way beyond the scope of this README. There's hardware compatibility to consider, drivers to install... There's a lot. So you're just going to have to figure this part out on your own if you want a GPU. But at least from a Python package perspective, the above requirements file has you covered. 56 | 57 | **Pretrained Weights** 58 | 59 | For the latest pretrained weights, check out the releases here on GitHub. 60 | 61 | **Training Data** You can find our training data here: 62 | 63 | https://www.kaggle.com/altf42600/pentest-screensots 64 | 65 | There's two things you need from the training data: 66 | 67 | 1. `images/` folder, containing all the screenshots (resized down to 224x224) 68 | 2. `labels.csv` that has all the labels 69 | 70 | Copy both into the root of the Eyeballer code tree. 71 | 72 | Additionally, you can find a pretrained weights file you can use right out of the box without training. 73 | 74 | 1. `bishop-fox-pretrained-vN.h5` 75 | 76 | It's here on GitHub, look at the `releases` section for the latest one. 77 | 78 | ## Predicting Labels 79 | 80 | NOTE: For best results, make sure you screenshot your websites in a native 1.6x aspect ratio. IE: 1440x900. Eyeballer will scale the image down automatically to the right size for you, but if it's the wrong aspect ratio then it will squish in a way that will affect prediction performance. 81 | 82 | To eyeball some screenshots, just run the "predict" mode: 83 | 84 | ``` 85 | eyeballer.py --weights YOUR_WEIGHTS.h5 predict YOUR_FILE.png 86 | ``` 87 | 88 | Or for a whole directory of files: 89 | 90 | ``` 91 | eyeballer.py --weights YOUR_WEIGHTS.h5 predict PATH_TO/YOUR_FILES/ 92 | ``` 93 | 94 | Eyeballer will spit the results back to you in human readable format (a `results.html` file so you can browse it easily) and machine readable format (a `results.csv` file). 95 | 96 | ## Performance 97 | 98 | Eyeballer's performance is measured against an evaluation dataset, which is 20% of the overall screenshots chosen at random. Since these screenshots are never used in training, they can be an effective way to see how well the model is performing. Here are the latest results: 99 | 100 | | Overall Binary Accuracy | 93.52% | 101 | | ------ |:-----:| 102 | | **All-or-Nothing Accuracy** | **76.09%** | 103 | 104 | **Overall Binary Accuracy** is probably what you think of as the model's "accuracy". It's the chance, given any single label, that it is correct. 105 | 106 | **All-or-Nothing Accuracy** is more strict. For this, we consider all of an image's labels and consider it a failure if ANY label is wrong. This accuracy rating is the chance that the model correctly predicts all labels for any given image. 107 | 108 | | Label | Precision | Recall | 109 | | ------ | ------ |:-----:| 110 | | Custom 404 | 80.20% | 91.01% | 111 | | Login Page | 86.41% | 88.47% | 112 | | Webapp | 95.32% | 96.83% | 113 | | Old Looking | 91.70% | 62.20% | 114 | | Parked Domain | 70.99% | 66.43% | 115 | 116 | For a detailed explanation on [Precision vs Recall, check out Wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall). 117 | 118 | ## Training 119 | To train a new model, run: 120 | ``` 121 | eyeballer.py train 122 | ``` 123 | 124 | You'll want a machine with a good GPU for this to run in a reasonable amount of time. Setting that up is outside the scope of this readme, however. 125 | 126 | This will output a new model file (weights.h5 by default). 127 | 128 | ## Evaluation 129 | 130 | You just trained a new model, cool! Let's see how well it performs against some images it's never seen before, across a variety of metrics: 131 | 132 | ``` 133 | eyeballer.py --weights YOUR_WEIGHTS.h5 evaluate 134 | ``` 135 | 136 | The output will describe the model's accuracy in both recall and precision for each of the program's labels. (Including "none of the above" as a pseudo-label) 137 | -------------------------------------------------------------------------------- /docs/404.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/docs/404.png -------------------------------------------------------------------------------- /docs/eyeballer_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/docs/eyeballer_logo.png -------------------------------------------------------------------------------- /docs/homepage.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/docs/homepage.png -------------------------------------------------------------------------------- /docs/login.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/docs/login.png -------------------------------------------------------------------------------- /docs/old-looking.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/docs/old-looking.png -------------------------------------------------------------------------------- /docs/parked.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/docs/parked.png -------------------------------------------------------------------------------- /eyeballer.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import click 4 | import csv 5 | 6 | from eyeballer.model import EyeballModel, DATA_LABELS 7 | from eyeballer.visualization import HeatMap 8 | from jinja2 import Template 9 | 10 | 11 | @click.group(invoke_without_command=True) 12 | @click.option('--weights', default=None, type=click.Path(), help="Weights file for input/output") 13 | @click.option('--summary/--no-summary', default=False, help="Print model summary at start") 14 | @click.option('--seed', default=None, type=int, help="RNG seed for data shuffling and transformations, defaults to random value") 15 | @click.pass_context 16 | def cli(ctx, weights, summary, seed): 17 | model_kwargs = {"weights_file": weights, 18 | "print_summary": summary, 19 | "seed": seed} 20 | 21 | # pass the model to subcommands 22 | ctx.ensure_object(dict) 23 | # We only pass the kwargs so we can be lazy and make the model later after the subcommand cli is parsed. This 24 | # way, the user doesn't have to wait for tensorflow if they are just calling --help on a subcommand. 25 | ctx.obj['model_kwargs'] = model_kwargs 26 | 27 | 28 | @cli.command() 29 | @click.option('--graphs/--no-graphs', default=False, help="Save accuracy and loss graphs to file") 30 | @click.option('--epochs', default=20, type=int, help="Number of epochs") # TODO better help string 31 | @click.option('--batchsize', default=32, type=int, help="Batch size") # TODO better help string 32 | @click.pass_context 33 | def train(ctx, graphs, batchsize, epochs): 34 | model = EyeballModel(**ctx.obj['model_kwargs']) 35 | model.train(print_graphs=graphs, batch_size=batchsize, epochs=epochs) 36 | 37 | 38 | @cli.command() 39 | @click.argument('screenshot') 40 | @click.option('--heatmap', default=False, is_flag=True, help="Create a heatmap graphfor the prediction") 41 | @click.option('--threshold', default=.5, type=float, help="Threshold confidence for labeling") 42 | @click.pass_context 43 | def predict(ctx, screenshot, heatmap, threshold): 44 | model = EyeballModel(**ctx.obj['model_kwargs']) 45 | results = model.predict(screenshot) 46 | 47 | if heatmap: 48 | # Generate a heatmap 49 | HeatMap(screenshot, model, threshold).generate() 50 | 51 | if not results: 52 | print("Error: Input file does not exist") 53 | if len(results) == 1: 54 | print(results) 55 | else: 56 | with open("results.csv", "w", newline="") as csvfile: 57 | fieldnames = ["filename", "custom404", "login", "webapp", "oldlooking", "parked"] 58 | labelwriter = csv.DictWriter(csvfile, fieldnames=fieldnames) 59 | labelwriter.writeheader() 60 | labelwriter.writerows(results) 61 | 62 | print("Output written to results.csv") 63 | buildHTML(processResults(results, threshold)) 64 | print("HTML written to results.html") 65 | 66 | 67 | def processResults(results, threshold): 68 | '''Filter the initial results dictionary and reformat it for use in JS. 69 | 70 | Keyword arguments: 71 | results -- dictionary output from predict function 72 | 73 | ''' 74 | jsResults = {} 75 | 76 | for result in results: 77 | positiveTags = [] 78 | 79 | for label, label_info in result.items(): 80 | if (label == 'filename'): 81 | pass 82 | elif label_info > threshold: 83 | positiveTags.append(label) 84 | 85 | jsResults[result['filename']] = positiveTags 86 | return(jsResults) 87 | 88 | 89 | def buildHTML(jsResults): 90 | '''Build HTML around the JS Dictionary that is passed from processResults. 91 | 92 | Keyword arguments: 93 | jsResults -- dictionary output from processResults function 94 | ''' 95 | html_output = "" 96 | with open("prediction_output_template.html") as template_file: 97 | template = Template(template_file.read()) 98 | html_output = template.render(jsResults=jsResults) 99 | 100 | with open('results.html', 'w') as file: 101 | file.write(html_output) 102 | 103 | 104 | def pretty_print_evaluation(results): 105 | """Print a human-readable summary of the evaluation""" 106 | # We use 4.2% to handle all the way from " 0.00%" (7chars) to "100.00%" (7chars) 107 | for label in DATA_LABELS: 108 | print("{} Precision Score: {:4.2%}".format(label, results[label]['precision'])) 109 | print("{} Recall Score: {:4.2%}".format(label, results[label]['recall'])) 110 | print("'None of the above' Precision: {:4.2%}".format(results['none_of_the_above_precision'])) 111 | print("'None of the above' Recall: {:4.2%}".format(results['none_of_the_above_recall'])) 112 | print("All or nothing Accuracy: {:4.2%}".format(results['all_or_nothing_accuracy'])) 113 | print("Overall Binary Accuracy: {:4.2%}".format(results['total_binary_accuracy'])) 114 | print("Top 10 worst predictions: {}".format(results['top_10_worst'][1])) 115 | 116 | 117 | @cli.command() 118 | @click.option('--threshold', default=.5, type=float, help="Threshold confidence for labeling") 119 | @click.pass_context 120 | def evaluate(ctx, threshold): 121 | model = EyeballModel(**ctx.obj['model_kwargs']) 122 | results = model.evaluate(threshold) 123 | pretty_print_evaluation(results) 124 | 125 | 126 | if __name__ == '__main__': 127 | cli() 128 | -------------------------------------------------------------------------------- /eyeballer/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/eyeballer/__init__.py -------------------------------------------------------------------------------- /eyeballer/augmentation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from Augmentor.Operations import Operation 4 | from tensorflow.keras.applications.mobilenet import preprocess_input 5 | 6 | 7 | class EyeballerAugmentation(Operation): 8 | def __init__(self, probability=1): 9 | Operation.__init__(self, probability) 10 | 11 | # Class must implement the perform_operation method 12 | def perform_operation(self, images): 13 | return_list = [] 14 | for image in images: 15 | image_array = np.array(image).astype('uint8') 16 | image_array = preprocess_input(image_array) 17 | return_list.append(image_array) 18 | return return_list 19 | -------------------------------------------------------------------------------- /eyeballer/model.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | import sys 4 | import progressbar 5 | 6 | # Prevent Tkinter Dependency 7 | import matplotlib 8 | matplotlib.use('agg') # noqa: E402 9 | import matplotlib.pyplot as plt 10 | 11 | import numpy as np 12 | import pandas as pd 13 | import Augmentor 14 | import tensorflow as tf 15 | 16 | from tensorflow.keras.applications.mobilenet import preprocess_input 17 | from sklearn.metrics import classification_report, accuracy_score, hamming_loss 18 | from eyeballer.augmentation import EyeballerAugmentation 19 | 20 | DATA_LABELS = ["custom404", "login", "webapp", "oldlooking", "parked"] 21 | 22 | 23 | class EyeballModel: 24 | """The primary model class of Eyeballer. 25 | 26 | Contains high-level functions for training, evaluating, and predicting. 27 | """ 28 | graphs_directory = "graphs/" 29 | checkpoint_file = "weights.h5" 30 | image_dir = "images/" 31 | image_width, image_height = 224, 224 32 | input_shape = (image_width, image_height, 3) 33 | 34 | def __init__(self, weights_file, print_summary=False, seed=None, quiet=False): 35 | """Constructor for model class. 36 | 37 | Keyword arguments: 38 | print_summary -- Whether or not to print to stdout the keras model summary, containing a detailed description of every model layer 39 | weights_file -- A filename for where to load the model's weights. 40 | seed -- PRNG seed, useful for repeating a previous run and using the same data. Training/Validation split is determined randomly. 41 | """ 42 | # # Build the model 43 | self.model = tf.keras.Sequential() 44 | pretrained_layer = tf.keras.applications.mobilenet.MobileNet(weights='imagenet', include_top=False, input_shape=self.input_shape) 45 | self.model.add(pretrained_layer) 46 | self.model.add(tf.keras.layers.GlobalAveragePooling2D()) 47 | self.model.add(tf.keras.layers.Dense(256, activation="relu")) 48 | self.model.add(tf.keras.layers.Dropout(0.5)) 49 | self.model.add(tf.keras.layers.Dense(128, activation="relu")) 50 | self.model.add(tf.keras.layers.Dropout(0.2)) 51 | self.model.add(tf.keras.layers.Dense(len(DATA_LABELS), activation="sigmoid")) 52 | 53 | self.model.compile(optimizer=tf.keras.optimizers.Adam(0.0005), 54 | loss="binary_crossentropy", 55 | metrics=["accuracy"]) 56 | 57 | if weights_file is not None and os.path.isfile(weights_file): 58 | try: 59 | self.model.load_weights(weights_file) 60 | except OSError: 61 | print("ERROR: Unable to open weights file '{}'".foramt(weights_file)) 62 | sys.exit(-1) 63 | print("Loaded model from file.") 64 | else: 65 | if weights_file is not None: 66 | raise FileNotFoundError 67 | print("WARN: No model loaded from file. Generating random model") 68 | 69 | if print_summary: 70 | print(self.model.summary()) 71 | 72 | self.quiet = quiet 73 | self.seed = seed 74 | 75 | def _init_labels(self): 76 | # Pull out our labels for use in generators later 77 | data = pd.read_csv("labels.csv") 78 | self.training_labels = data.loc[data['evaluation'] == False] # noqa: E712 79 | self.evaluation_labels = data.loc[data['evaluation'] == True] # noqa: E712 80 | 81 | # Shuffle the training labels 82 | self.random_seed = False 83 | if self.seed is None: 84 | self.random_seed = True 85 | self.seed = random.randint(0, 999999) 86 | print("No seed set, ", end='') 87 | print("using seed: {}".format(self.seed)) 88 | random.seed(self.seed) 89 | self.training_labels = self.training_labels.sample(frac=1) 90 | 91 | # Data augmentation 92 | augmentor = Augmentor.Pipeline() 93 | augmentor.set_seed(self.seed) 94 | augmentor.zoom(probability=0.75, min_factor=0.8, max_factor=1.2) 95 | augmentor.random_color(probability=0.75, min_factor=0.5, max_factor=1.0) 96 | augmentor.random_contrast(probability=0.75, min_factor=0.8, max_factor=1.0) 97 | augmentor.random_brightness(probability=0.75, min_factor=0.8, max_factor=1.2) 98 | augmentor.random_erasing(probability=0.75, rectangle_area=0.15) 99 | 100 | # Finalizes the augmentation with a custom operation to prepare the image for the specific pretrained model we're using 101 | training_augmentation = EyeballerAugmentation() 102 | augmentor.add_operation(training_augmentation) 103 | self.preprocess_training_function = augmentor.keras_preprocess_func() 104 | 105 | def train(self, epochs=20, batch_size=32, print_graphs=False): 106 | """Train the model, making a new weights file at each successfull checkpoint. You'll probably need a GPU for this to realistically run. 107 | 108 | Keyword arguments: 109 | epochs -- The number of epochs to train for. (An epoch is one pass-through of the dataset) 110 | batch_size -- How many images to batch together when training. Generally speaking, the higher the better, until you run out of memory. 111 | print_graphs --- Whether or not to create accuracy and loss graphs. If true, they'll be written to accuracy.png and loss.png 112 | """ 113 | print("Training with seed: " + str(self.seed)) 114 | 115 | self._init_labels() 116 | data_generator = tf.keras.preprocessing.image.ImageDataGenerator( 117 | preprocessing_function=self.preprocess_training_function, 118 | validation_split=0.2, 119 | samplewise_center=True) 120 | 121 | training_generator = data_generator.flow_from_dataframe( 122 | self.training_labels, 123 | directory=self.image_dir, 124 | x_col="filename", 125 | y_col=DATA_LABELS, 126 | target_size=(self.image_width, self.image_height), 127 | batch_size=batch_size, 128 | subset='training', 129 | shuffle=True, 130 | seed=self.seed, 131 | class_mode="raw") 132 | validation_generator = data_generator.flow_from_dataframe( 133 | self.training_labels, 134 | directory=self.image_dir, 135 | x_col="filename", 136 | y_col=DATA_LABELS, 137 | target_size=(self.image_width, self.image_height), 138 | batch_size=batch_size, 139 | subset='validation', 140 | shuffle=False, 141 | seed=self.seed, 142 | class_mode="raw") 143 | 144 | # Model checkpoint - Saves model weights when validation accuracy improves 145 | callbacks = [tf.keras.callbacks.ModelCheckpoint(self.checkpoint_file, 146 | monitor='val_loss', 147 | verbose=1, 148 | save_best_only=True, 149 | save_weights_only=True, 150 | mode='min'), 151 | tf.keras.callbacks.TensorBoard(log_dir='logs', 152 | histogram_freq=2, 153 | write_graph=True, 154 | write_images=False, 155 | update_freq='epoch', 156 | profile_batch=2, 157 | embeddings_freq=0, 158 | embeddings_metadata=None), 159 | ] 160 | 161 | history = self.model.fit( 162 | training_generator, 163 | steps_per_epoch=len(training_generator.filenames) // batch_size, 164 | epochs=epochs, 165 | validation_data=validation_generator, 166 | validation_steps=len(validation_generator.filenames) // batch_size, 167 | callbacks=callbacks, 168 | verbose=1) 169 | 170 | if print_graphs: 171 | if not os.path.exists(self.graphs_directory): 172 | os.makedirs(self.graphs_directory) 173 | # Plot training & validation accuracy values 174 | plt.plot(history.history['acc']) 175 | plt.plot(history.history['val_acc']) 176 | plt.title('Model accuracy') 177 | plt.ylabel('Accuracy') 178 | plt.xlabel('Epoch') 179 | plt.legend(['Train', 'Validation'], loc='upper left') 180 | plt.savefig(self.graphs_directory + "accuracy.png") 181 | plt.clf() 182 | plt.cla() 183 | plt.close() 184 | 185 | # Plot training & validation loss values 186 | plt.plot(history.history['loss']) 187 | plt.plot(history.history['val_loss']) 188 | plt.title('Model loss') 189 | plt.ylabel('Loss') 190 | plt.xlabel('Epoch') 191 | plt.legend(['Train', 'Validation'], loc='upper left') 192 | plt.savefig(self.graphs_directory + "loss.png") 193 | 194 | def predict_on_array(self, image): 195 | """Predict the labels for a single screenshot 196 | 197 | Keyword arguments: 198 | image -- The numpy array of the image to classify 199 | """ 200 | img = np.expand_dims(image, axis=0) 201 | img = preprocess_input(img) 202 | 203 | prediction = self.model.predict(img, batch_size=1) 204 | result = dict() 205 | result["filename"] = "custom-image" 206 | result["custom404"] = prediction[0][0] 207 | result["login"] = prediction[0][1] 208 | result["webapp"] = prediction[0][2] 209 | result["oldlooking"] = prediction[0][3] 210 | result["parked"] = prediction[0][4] 211 | return result 212 | 213 | def predict(self, path, threshold=0.5): 214 | """Predict the labels for a single file or directory of files 215 | 216 | Keyword arguments: 217 | path -- The path to the file(s) that we'll be evaluating. 218 | """ 219 | # Is this a single file, or a directory? 220 | screenshots = [] 221 | if os.path.isfile(path): 222 | screenshots = [path] 223 | elif os.path.isdir(path): 224 | screenshots = os.listdir(path) 225 | screenshots = [os.path.join(path, s) for s in screenshots] 226 | else: 227 | raise FileNotFoundError 228 | 229 | results = [] 230 | bar = progressbar.ProgressBar() 231 | if self.quiet: 232 | bar = progressbar.NullBar() 233 | for screenshot in bar(screenshots): 234 | # Load the image into memory 235 | img = None 236 | try: 237 | img = tf.keras.preprocessing.image.load_img(screenshot, target_size=(self.image_width, self.image_height)) 238 | img = tf.keras.preprocessing.image.img_to_array(img) 239 | img = np.expand_dims(img, axis=0) 240 | img = preprocess_input(img) 241 | except IsADirectoryError: 242 | print("\nWARN: Skipping directory: ", screenshot) 243 | continue 244 | except OSError: 245 | print("\nWARN: Skipping empty or corrupt file: ", screenshot) 246 | continue 247 | 248 | prediction = self.model.predict(img, batch_size=1) 249 | result = dict() 250 | result["filename"] = screenshot 251 | result["custom404"] = prediction[0][0] 252 | result["login"] = prediction[0][1] 253 | result["webapp"] = prediction[0][2] 254 | result["oldlooking"] = prediction[0][3] 255 | result["parked"] = prediction[0][4] 256 | results.append(result) 257 | return results 258 | 259 | def evaluate(self, threshold=0.5): 260 | """Evaluate performance against the persistent evaluation data set 261 | 262 | Keyword arguments: 263 | threshold -- Value between 0->1. The cutoff where the numerical prediction becomes boolean. Default: 0.5 264 | """ 265 | # Prepare the data 266 | self._init_labels() 267 | data_generator = tf.keras.preprocessing.image.ImageDataGenerator( 268 | preprocessing_function=preprocess_input) 269 | evaluation_generator = data_generator.flow_from_dataframe( 270 | self.evaluation_labels, 271 | directory=self.image_dir, 272 | x_col="filename", 273 | y_col=DATA_LABELS, 274 | target_size=(self.image_width, self.image_height), 275 | shuffle=False, 276 | batch_size=1, 277 | class_mode="raw") 278 | # If a seed was selected, then also evaluate on the validation set for that seed 279 | if not self.random_seed: 280 | print("Using validation set...") 281 | # Data augmentation 282 | data_generator = tf.keras.preprocessing.image.ImageDataGenerator( 283 | preprocessing_function=self.preprocess_training_function, 284 | samplewise_center=True, 285 | validation_split=0.2) 286 | evaluation_generator = data_generator.flow_from_dataframe( 287 | self.training_labels, 288 | directory=self.image_dir, 289 | x_col="filename", 290 | y_col=DATA_LABELS, 291 | target_size=(self.image_width, self.image_height), 292 | batch_size=1, 293 | subset='validation', 294 | shuffle=False, 295 | seed=self.seed, 296 | class_mode="raw") 297 | else: 298 | print("Using evaluation set...") 299 | 300 | predictions = self.model.predict( 301 | evaluation_generator, 302 | verbose=1, 303 | steps=len(evaluation_generator)) 304 | 305 | self._save_prediction_histograms(predictions) 306 | predictions = predictions > threshold 307 | ground_truth = self.evaluation_labels[DATA_LABELS].to_numpy() 308 | filenames = self.evaluation_labels[["filename"]].to_numpy() 309 | stats = classification_report(ground_truth, predictions, target_names=DATA_LABELS, output_dict=True) 310 | stats["total_binary_accuracy"] = 1 - hamming_loss(ground_truth, predictions) 311 | stats["all_or_nothing_accuracy"] = accuracy_score(ground_truth, predictions) 312 | stats["top_10_best"] = self._top_images(filenames, ground_truth, predictions, best=True) 313 | stats["top_10_worst"] = self._top_images(filenames, ground_truth, predictions, best=False) 314 | stats["none_of_the_above_recall"] = self._none_of_the_above_recall(ground_truth, predictions) 315 | stats["none_of_the_above_precision"] = self._none_of_the_above_precision(ground_truth, predictions) 316 | return stats 317 | 318 | def _none_of_the_above_recall(self, labels, predictions): 319 | """Returns the recall score for the 'none of the above' images. 320 | That means, all the images that don't have a category. 321 | """ 322 | total_count = 0 323 | correct_count = 0 324 | for item in zip(labels.astype(int), predictions.astype(int)): 325 | # Is this a none of the above? 326 | if not item[0].any(): 327 | total_count += 1 328 | if not item[1].any(): 329 | correct_count += 1 330 | if total_count == 0: 331 | print("WARNING: None of the Above Recall is NaN") 332 | return 0 333 | return correct_count / total_count 334 | 335 | def _none_of_the_above_precision(self, labels, predictions): 336 | """Returns the precision score for the 'none of the above' images. 337 | That means, all the images that don't have a category. 338 | """ 339 | total_count = 0 340 | correct_count = 0 341 | for item in zip(labels.astype(int), predictions.astype(int)): 342 | # Is this a none of the above prediction? 343 | if not item[1].any(): 344 | total_count += 1 345 | if not item[0].any(): 346 | correct_count += 1 347 | if total_count == 0: 348 | print("WARNING: None of the Above Precision is NaN") 349 | return 0 350 | return correct_count / total_count 351 | 352 | def _top_images(self, filenames, ground_truth, predictions, top_k=10, best=False): 353 | """Collect top-k best or top-k worst predicted images 354 | 355 | Keyword arguments: 356 | ground_truth -- The correct labels 357 | predictions -- The numpy array of predictions 358 | top_k -- Top k elements 359 | best -- True/False. Calculate either the best or worst images 360 | 361 | :Return -- Tuple of top-k indicies and top-k filenames 362 | """ 363 | true_labels = np.array(ground_truth).astype(float) 364 | predictions = predictions.astype(float) 365 | 366 | differences = np.abs(predictions - true_labels).sum(axis=1) 367 | indicies = np.argsort(differences, axis=0) 368 | 369 | top_file_list = [] 370 | 371 | if not best: 372 | # Reverse numpy array 373 | indicies = np.flipud(indicies) 374 | 375 | for i in indicies[:top_k]: 376 | top_file_list.append(filenames[i][0]) 377 | 378 | return indicies[:top_k], top_file_list 379 | 380 | def _save_prediction_histograms(self, predictions, buckets=50): 381 | """Saves a series of histogram screenshots 382 | 383 | Keyword arguments: 384 | predictions -- The numpy array of predicted labels, ranging from 0->1. 385 | buckets -- The number of buckets to divide the data into. Default: 50 386 | :Returns -- Nothing" 387 | """ 388 | figure, axes = plt.subplots(nrows=len(DATA_LABELS)) 389 | for i, label in enumerate(DATA_LABELS): 390 | axes[i].hist(predictions[:, i], buckets, alpha=.75) 391 | axes[i].set_xlabel("Prediction") 392 | axes[i].set_ylabel("Counts of Predictions") 393 | axes[i].set_title(label) 394 | axes[i].grid(True) 395 | figure.set_size_inches(5, 3*len(DATA_LABELS)) 396 | figure.tight_layout() 397 | if not os.path.exists(self.graphs_directory): 398 | os.makedirs(self.graphs_directory) 399 | plt.savefig(self.graphs_directory + "label_histograms.png") 400 | -------------------------------------------------------------------------------- /eyeballer/visualization.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import math 3 | import tensorflow as tf 4 | 5 | from copy import deepcopy 6 | from matplotlib import pyplot as plt 7 | from eyeballer.model import DATA_LABELS 8 | 9 | 10 | class HeatMap(): 11 | def __init__(self, screenshot_path, model, threshold=0.5, boxsize=28, step=7): 12 | self.model = model 13 | 14 | img = tf.keras.preprocessing.image.load_img(screenshot_path, target_size=(self.model.image_width, self.model.image_height)) 15 | img = tf.keras.preprocessing.image.img_to_array(img) 16 | 17 | self.screenshot = img 18 | self.boxsize = boxsize 19 | self.step = step 20 | self.x = 0 21 | self.y = 0 22 | self.screenshot_path = screenshot_path 23 | self.gamma = 3 24 | self.threshold = threshold 25 | 26 | def generate(self, output_file="heatmap.png"): 27 | """ Make a single heatmap image and return it """ 28 | heatmaps = [] 29 | labels = [] 30 | results = self.model.predict(self.screenshot_path) 31 | results = results[0] 32 | for label in DATA_LABELS: 33 | boxsize = self.boxsize 34 | # Ignore this label if it didn't predict positively (true label) 35 | if results[label] > self.threshold: 36 | worst_score = 1 37 | while worst_score > self.threshold: 38 | heatmap, worst_score = self._get_heatmap(label, boxsize) 39 | if worst_score > self.threshold: 40 | boxsize += 28 41 | print("Didn't get a good image for {}. Trying again with a bigger boxsize: {}".format(label, boxsize)) 42 | else: 43 | heatmaps.append(heatmap) 44 | labels.append(label) 45 | 46 | plt.figure() 47 | screenshot_image = tf.keras.preprocessing.image.load_img(self.screenshot_path, target_size=(self.model.image_width, self.model.image_width)) 48 | _, subplots = plt.subplots(1, len(heatmaps)) 49 | # This is a little janky, but matplotlib returns a list above if there's multiple subplots, and just a single subplot if there's only one 50 | # it would have been easier if it was always a list. But alas 51 | if not heatmaps: 52 | print("No heatmap made. The image did not have a True classification for any label") 53 | return 54 | if len(heatmaps) > 1: 55 | for i, heatmap in enumerate(heatmaps): 56 | subplots[i].imshow(screenshot_image, cmap='binary', interpolation='none') 57 | subplots[i].imshow(heatmap, alpha=0.5, interpolation='none') 58 | subplots[i].set_title(labels[i]) 59 | else: 60 | subplots.imshow(screenshot_image, cmap='binary', interpolation='none') 61 | subplots.imshow(heatmap, alpha=0.5, interpolation='none') 62 | subplots.set_title(labels[0]) 63 | plt.savefig(output_file) 64 | print("Heatmap image written to: {}".format(output_file)) 65 | 66 | def _get_heatmap(self, label, boxsize): 67 | worst_score = 1 68 | self.x, self.y = 0, 0 69 | 70 | heatmap = np.ones((self.model.image_width, self.model.image_height)) 71 | heatmap *= 255 72 | while True: 73 | # Occlude an image 74 | occluded_image, x, y = self._occlude(boxsize) 75 | if occluded_image is not None: 76 | # Score the new occluded image 77 | results = self.model.predict_on_array(occluded_image) 78 | score = results[label] # TODO 79 | worst_score = min(worst_score, score) 80 | # Scale the score up to pixel values 81 | score = math.floor(score * 255) 82 | # Anneal the scores as you go outward into the occlusion block 83 | new_scores = np.ones((boxsize, boxsize)) 84 | new_scores *= score 85 | new_scores = self._gamma_anneal(new_scores) 86 | 87 | # For each pixel, update only where the new score is lower 88 | original_scores = heatmap[x: x+boxsize, y: y+boxsize] 89 | 90 | target_area = np.where(new_scores < original_scores, new_scores, original_scores) 91 | heatmap[x: x+boxsize, y: y+boxsize] = target_area 92 | else: 93 | break 94 | 95 | return heatmap, worst_score 96 | 97 | def _gamma_anneal(self, occlusion_area): 98 | """ Get the annealed score based on centeredness of the occlusion area """ 99 | width, height = occlusion_area.shape 100 | 101 | center_x = (width-1) / 2 102 | center_y = (height-1) / 2 103 | 104 | annealed_area = np.zeros(occlusion_area.shape) 105 | 106 | for (x, y), score in np.ndenumerate(occlusion_area): 107 | distance = math.sqrt((center_x - x)**2 + (center_y - y)**2) 108 | annealed_area[x, y] = min(255, score + (self.gamma * distance)) 109 | 110 | return annealed_area 111 | 112 | def _occlude(self, boxsize): 113 | ''' Return a single occluded image and its location ''' 114 | if self.x + boxsize > self.screenshot.shape[0]: 115 | return None, None, None 116 | 117 | retImg = np.copy(self.screenshot) 118 | retImg[self.x:self.x+boxsize, self.y:self.y+boxsize] = 0.0 119 | 120 | old_i = deepcopy(self.x) 121 | old_j = deepcopy(self.y) 122 | 123 | # update indices 124 | self.y = self.y + self.step 125 | if self.y+boxsize > self.screenshot.shape[1]: # reached end 126 | self.y = 0 # reset j 127 | self.x = self.x + self.step # go to next row 128 | 129 | return retImg, old_i, old_j 130 | -------------------------------------------------------------------------------- /prediction_output_template.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Eyeballer Results 5 | 6 |
7 | 8 | 9 | 10 | 11 | 12 | 13 | 19 |
20 |
21 |
22 |
23 |
24 | 25 | 104 | 189 | 190 | 191 | -------------------------------------------------------------------------------- /requirements-gpu.txt: -------------------------------------------------------------------------------- 1 | Augmentor 2 | click 3 | matplotlib 4 | numpy 5 | pandas 6 | pillow 7 | sklearn 8 | tensorflow-gpu 9 | jinja2 10 | progressbar2 11 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Augmentor 2 | click 3 | matplotlib 4 | numpy 5 | pandas 6 | pillow 7 | scikit-learn 8 | tensorflow 9 | jinja2 10 | progressbar2 11 | scipy 12 | -------------------------------------------------------------------------------- /tests/PredictTest.py: -------------------------------------------------------------------------------- 1 | import errno 2 | import os 3 | import unittest 4 | import sys 5 | 6 | from eyeballer.model import EyeballModel 7 | from eyeballer.visualization import HeatMap 8 | 9 | 10 | class PredictTest(unittest.TestCase): 11 | def setUp(self): 12 | 13 | class DummyFile(object): 14 | def write(self, x): pass 15 | def flush(self): pass 16 | 17 | sys.stdout = DummyFile() 18 | weights_file = "tests/models/test_weights.h5" 19 | if not os.path.isfile(weights_file): 20 | print("Error: Symlink the latest weights file to " + weights_file) 21 | raise FileNotFoundError( 22 | errno.ENOENT, 23 | os.strerror(errno.ENOENT), 24 | weights_file) 25 | 26 | model_kwargs = { 27 | "weights_file": weights_file, 28 | "print_summary": False, 29 | "seed": None, 30 | "quiet": True 31 | } 32 | self.model = EyeballModel(**model_kwargs) 33 | 34 | def test_different_seed_predict(self): 35 | model_kwargs = { 36 | "weights_file": None, 37 | "print_summary": False, 38 | "seed": 12345678, 39 | "quiet": True 40 | } 41 | same_seed_model = EyeballModel(**model_kwargs) 42 | 43 | screenshot = "tests/data/404.png" 44 | 45 | results_one = self.model.predict(screenshot) 46 | results_two = same_seed_model.predict(screenshot) 47 | 48 | self.assertNotEqual(results_one, results_two) 49 | 50 | def test_same_seed_predict(self): 51 | model_kwargs = { 52 | "weights_file": None, 53 | "print_summary": False, 54 | "seed": 12345678, 55 | "quiet": True 56 | } 57 | same_seed_model = EyeballModel(**model_kwargs) 58 | 59 | screenshot = "tests/data/404.png" 60 | 61 | results_one = same_seed_model.predict(screenshot) 62 | results_two = same_seed_model.predict(screenshot) 63 | 64 | self.assertEqual(results_one, results_two) 65 | 66 | def test_predict_custom404(self): 67 | screenshot = "tests/data/404.png" 68 | results = self.model.predict(screenshot)[0] 69 | self.assertGreater(results["custom404"], 0.5) 70 | 71 | def test_predict_not_custom404(self): 72 | screenshot = "tests/data/nothing.png" 73 | results = self.model.predict(screenshot)[0] 74 | self.assertLess(results["custom404"], 0.5) 75 | 76 | def test_predict_login(self): 77 | screenshot = "tests/data/login.png" 78 | results = self.model.predict(screenshot)[0] 79 | self.assertGreater(results["login"], 0.5) 80 | 81 | def test_predict_not_login(self): 82 | screenshot = "tests/data/nothing.png" 83 | results = self.model.predict(screenshot)[0] 84 | print(screenshot, results) 85 | self.assertLess(results["login"], 0.5) 86 | 87 | def test_predict_homepage(self): 88 | screenshot = "tests/data/homepage.png" 89 | results = self.model.predict(screenshot)[0] 90 | self.assertGreater(results["homepage"], 0.5) 91 | 92 | def test_predict_not_homepage(self): 93 | screenshot = "tests/data/nothing.png" 94 | results = self.model.predict(screenshot)[0] 95 | self.assertLess(results["homepage"], 0.5) 96 | 97 | def test_predict_oldlooking(self): 98 | screenshot = "tests/data/old-looking.png" 99 | results = self.model.predict(screenshot)[0] 100 | self.assertGreater(results["oldlooking"], 0.5) 101 | 102 | def test_predict_not_oldlooking(self): 103 | screenshot = "tests/data/nothing.png" 104 | results = self.model.predict(screenshot)[0] 105 | self.assertLess(results["oldlooking"], 0.5) 106 | 107 | def test_file_doesnt_exist(self): 108 | screenshot = "tests/data/doesnotexist.png" 109 | try: 110 | self.model.predict(screenshot)[0] 111 | self.fail("FileNotFoundError was expected but not found") 112 | except FileNotFoundError: 113 | pass 114 | 115 | def test_folder(self): 116 | screenshots = "tests/data/" 117 | results = self.model.predict(screenshots) 118 | self.assertEqual(len(results), 7) 119 | 120 | def test_file_is_empty(self): 121 | """ 122 | We're just testing that it doesn't crash, basically 123 | """ 124 | screenshot = "tests/data/empty.png" 125 | self.model.predict(screenshot) 126 | 127 | def test_file_is_invalid(self): 128 | """ 129 | We're just testing that it doesn't crash, basically 130 | """ 131 | screenshot = "tests/data/invalid.png" 132 | self.model.predict(screenshot) 133 | 134 | def test_heatmap(self): 135 | screenshot = "tests/data/login.png" 136 | HeatMap(screenshot, self.model, 0.5) 137 | 138 | screenshot = "tests/data/404.png" 139 | HeatMap(screenshot, self.model, 0.5) 140 | 141 | screenshot = "tests/data/empty.png" 142 | HeatMap(screenshot, self.model, 0.5) 143 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | from tests.PredictTest import * # noqa 2 | -------------------------------------------------------------------------------- /tests/data/404.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/tests/data/404.png -------------------------------------------------------------------------------- /tests/data/empty.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /tests/data/small.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/tests/data/small.png -------------------------------------------------------------------------------- /tests/models/test_weights.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BishopFox/eyeballer/52992277a13c1cc4b8d9e37c050f0c6c8ed7d94f/tests/models/test_weights.h5 -------------------------------------------------------------------------------- /utils/labelbox_to_labels.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import csv 4 | import random 5 | import json 6 | 7 | with open("newlabels.csv", "w", newline="") as csvfile: 8 | # Open the old labels file 9 | with open("labelbox.csv", newline="") as oldfile: 10 | # Get the header labels 11 | csvreader = csv.DictReader(oldfile) 12 | fieldnames = next(csvreader) 13 | labelwriter = csv.DictWriter(csvfile, fieldnames=["filename", "login", "custom404", "homepage", "oldlooking", "evaluation"]) 14 | labelwriter.writeheader() 15 | 16 | # Loop through the file 17 | rows = [] 18 | for row in csvreader: 19 | filename = row["External ID"] 20 | print(row["Label"]) 21 | labelstring = row["Label"] 22 | if row["Label"] == "Skip": 23 | labelstring = '{"imageclassification":[]}' 24 | labels = json.loads(labelstring) 25 | 26 | newrow = dict() 27 | newrow["filename"] = filename 28 | newrow["oldlooking"] = True 29 | newrow["login"] = "loginpage" in labels["imageclassification"] 30 | newrow["homepage"] = "homepage" in labels["imageclassification"] 31 | newrow["custom404"] = "custom404"in labels["imageclassification"] 32 | 33 | newrow["evaluation"] = random.random() > 0.8 34 | 35 | rows.append(newrow) 36 | labelwriter.writerows(rows) 37 | print("Made new labels file: newlabels.csv") 38 | -------------------------------------------------------------------------------- /utils/reroll.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import csv 4 | import random 5 | 6 | with open("newlabels.csv", "w", newline="") as csvfile: 7 | # Open the old labels file 8 | with open("labels.csv", newline="") as oldfile: 9 | # Get the header labels 10 | csvreader = csv.DictReader(oldfile) 11 | fieldnames = next(csvreader) 12 | labelwriter = csv.DictWriter(csvfile, fieldnames=fieldnames) 13 | labelwriter.writeheader() 14 | 15 | # Loop through the file 16 | rows = [] 17 | for row in csvreader: 18 | row["evaluation"] = random.random() > 0.8 19 | rows.append(row) 20 | labelwriter.writerows(rows) 21 | print("Made new labels file: newlabels.csv") 22 | -------------------------------------------------------------------------------- /utils/resizer.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Converts full-size images to a given size (replacing the originals) 4 | for f in images/* 5 | do 6 | echo $f 7 | convert -resize 224x224\! $f $f 8 | done 9 | -------------------------------------------------------------------------------- /utils/verify.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | import csv 4 | import random 5 | from os import listdir 6 | from os.path import isfile, join 7 | 8 | with open("labels.csv", newline="") as csvfile: 9 | # Get the header labels 10 | csvreader = csv.DictReader(csvfile) 11 | # fieldnames = next(csvreader) 12 | file_list = [f for f in listdir("images") if isfile(join("images", f))] 13 | 14 | label_list = [] 15 | for row in csvreader: 16 | label_list.append(row["filename"]) 17 | 18 | print(len(file_list), "files") 19 | print(len(label_list), "labels") 20 | 21 | # Loop through the file 22 | print("Rows in labels.csv, but no image exists:") 23 | for label in label_list: 24 | if label not in file_list: 25 | print(label) 26 | 27 | print("Images that exist, but don't have labels:") 28 | i = 0 29 | for image in file_list: 30 | if image not in label_list: 31 | print(image) 32 | --------------------------------------------------------------------------------