├── LICENSE ├── README.md ├── Results ├── 006_1.png ├── 029_2.png ├── 099_1.png ├── 121_1.png └── ReadMe ├── Test Images ├── 006_F.png ├── 048_F.png ├── 121_F.png └── 132_F.png ├── dct_kmeans.py ├── main.py └── sort.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. 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Finally, In the end, results produced are shown in terms of different parameters such as: (i) type of morphological operation on image, (ii) number of clusters used, (iii) correlation limit, (iv) distance limit. 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | -------------------------------------------------------------------------------- /Results/006_1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AR13ar/Unsupervised-Image-Forgery-Detection-Using-Discrete-Cosine-Transform-with-K-means-clustering/9da4992b99a89421bc5c5fd93f483fd9c8e45d18/Results/006_1.png -------------------------------------------------------------------------------- /Results/029_2.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /Results/ReadMe: -------------------------------------------------------------------------------- 1 | The results show a lot of false positives, hence much work is needed. 2 | -------------------------------------------------------------------------------- /Test Images/006_F.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AR13ar/Unsupervised-Image-Forgery-Detection-Using-Discrete-Cosine-Transform-with-K-means-clustering/9da4992b99a89421bc5c5fd93f483fd9c8e45d18/Test Images/006_F.png -------------------------------------------------------------------------------- /Test Images/048_F.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /dct_kmeans.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.fftpack import dct 3 | from sklearn.cluster import KMeans 4 | 5 | 6 | def dct_block(image): 7 | #dividing the gray-scale image into overlapping blocks of size 8x8 8 | filt_dim = 10 #filt_dim signifies the size of the window of the filter which will move on the image 9 | image = np.pad(image,int(filt_dim/2), mode = 'wrap' ) 10 | a,b = image.shape #image.shape->reading the size of the image 11 | win_data = [] 12 | stride = 2 #stride means how many steps the filter moves at a time i.e defines the overlapping 13 | for i, row in enumerate(range (0,a-filt_dim,stride)): 14 | for j, col in enumerate(range(0, b-filt_dim,stride)): 15 | win = image[row:row+filt_dim,col:col+filt_dim] 16 | win_data.append(win.flatten()) #append means adding to the list 17 | win_data = np.array(win_data) #np.array converts the list into 2-D array form 18 | #applying DCT on the image 19 | dct_list=[] 20 | for i in range(win_data.shape[0]): 21 | dct_list.append(dct(np.reshape(win_data[i],(filt_dim,filt_dim)), 1)) 22 | dct_list=np.array(dct_list) 23 | return dct_list, win_data 24 | 25 | 26 | def kmeans(dct_coeff_list_16): 27 | #applying KMeans 28 | labels = [] #to store labels of the clusters 29 | center = [] #to store centre of each cluster 30 | k_dct = np.array(dct_coeff_list_16) #converting 16 coeff list to K_dct array 31 | k_dct = k_dct.reshape(k_dct.shape[0], 16) #appending the 16 dct coeffs from all the blocks into K_dct array 32 | k_dct = (k_dct-k_dct.mean())/k_dct.std() 33 | clf=KMeans(20) #no. of clusters 34 | clf.fit(k_dct) 35 | labels.append( clf.labels_) #finding the labels 36 | center.append(clf.cluster_centers_) #finding the centres 37 | center = np.array(center) 38 | center = center.reshape(center.shape[1], center.shape[2]) 39 | labels = np.array(labels) 40 | labels = labels.T #taking transpose of the labels matrix 41 | return center, labels, k_dct 42 | 43 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from matplotlib import pyplot as plt 3 | import numpy as np 4 | from scipy.fftpack import dct 5 | from sklearn.cluster import KMeans 6 | from scipy.stats import pearsonr 7 | 8 | #reading the image and converting into gray-scale (0-> gray || 1->RGB) 9 | image = cv2.imread('D:\\PROJECTS\\pytorch_github\\project_2\\006_F.png',0) 10 | 11 | 12 | #resizing the image and viewing it 13 | image = cv2.resize(image,None,fx=0.4,fy=0.4) 14 | plt.imshow(image, cmap ='gray') 15 | 16 | #Thresholded image to boost accuracy 17 | #ret, image = cv2.threshold(image, 0,255, cv2.THRESH_OTSU) 18 | #cv2.THRESH_BINARY + 19 | #selecting the 16 zig-zag sequence of DCT coefficients 20 | x=[0,0,1,2,1,0,0,1,2,3,4,3,2,1,0,0] 21 | y=[0,1,0,0,1,2,3,2,1,0,0,1,2,3,4,5] 22 | #x = np.random.choice(16,8) 23 | #y = np.random.choice(16,8) 24 | 25 | dct_coeff_list_16 = [] 26 | dct_list, win_data = dct_block(image) 27 | for i in range(dct_list.shape[0]): 28 | dct_coeff=[] 29 | for j in range(len(x)): 30 | dct_coeff.append(dct_list[i,x[j],y[j]]) 31 | dct_coeff_list_16.append(np.array(dct_coeff)) 32 | 33 | 34 | center, labels, k_dct = kmeans(dct_coeff_list_16) 35 | 36 | #lines 71-77 are just meant to help backtracking by forming a new matrix con_cat by merging labels and K_dct 37 | aa = int(center.shape[0]) 38 | ll = np.zeros(aa) 39 | for i,row in enumerate(range(1,aa+1,1)): 40 | ll[i] = row 41 | ll = ll.reshape(ll.shape[0], 1) 42 | con_cat = np.concatenate((labels, k_dct), axis = 1) 43 | 44 | 45 | #fing the sum of the rows i.e sum of features of clusters row-wise to distinguish each row from another as 46 | # a result to help us sorting the feature vectors 47 | 48 | #find the sum of each row i.e sum of 16 coeff values of each block 49 | sum_ = [] 50 | for i in range(k_dct.shape[0]): 51 | sum_.append((k_dct[i]).sum()) 52 | 53 | sum_1 = np.array(sum_) 54 | sum_1 = sum_1.reshape(sum_1.shape[0],1) 55 | sum_11 = np.concatenate((labels, sum_1), axis = 1) #attaching labels list to their corresponding to row 56 | #sum values to ease the backtracking 57 | #fing the sum of all the centres 58 | sum_center = [] 59 | for i in range(center.shape[0]): 60 | sum_center.append((center[i]).sum()) 61 | 62 | sum_center = np.array(sum_center) #converting from list to matrix 63 | sum_center = sum_center.reshape(sum_center.shape[0],1) 64 | sum_center_11 = np.concatenate((ll,sum_center), axis = 1) #same what we were doing from line 89-90 65 | 66 | group = [] 67 | for i in range(sum_11.shape[0]): 68 | for j in range(sum_center_11.shape[0]): 69 | if(sum_11[i][0] == (sum_center_11[j][0] - 1)): 70 | group.append((sum_center_11[j][1] - sum_11[i][1])/center[j].std() ) #normalization of values 71 | 72 | 73 | #normalizing the value of sum we got coreesponding to each row using sum_centre 74 | group = np.array(group) 75 | group = group.reshape(group.shape[0], 1) 76 | group_1 = np.concatenate((labels, group), axis = 1) #attaching labels to normalised vales to ease back-track 77 | bb = int(win_data.shape[0]) 78 | ll_576 = np.zeros(bb) 79 | for i,row in enumerate(range(1,bb + 1,1)): 80 | ll_576[i] = row 81 | ll_576 = ll_576.reshape(ll_576.shape[0], 1) 82 | 83 | group_11 = np.concatenate((group_1, ll_576), axis = 1) 84 | con_cat_1 = np.concatenate((con_cat, group), axis = 1) 85 | 86 | #this function sorts the labels of the clusters i.e sorting the matrix based on the values of "label" coloumn 87 | ll_con_cat = np.concatenate((con_cat_1, ll_576), axis = 1) 88 | ll_con_cat_1 = lab_sort(ll_con_cat, aa) 89 | #this function sorts the rows (feature vectors) of a single cluster type at a time according to their 90 | #corresponding normalised sum values. this is done for each group(cluster type) 91 | ll_con_cat_11 = f_sort(ll_con_cat_1, aa) 92 | 93 | #correlation part 94 | #pearsonr is used to find the correlation 95 | corr = ll_con_cat_11[:,1:-2] 96 | 97 | #finding the correlation between ith row and (i+1)th row & storing in pear 98 | pear = [] 99 | for i in range(corr.shape[0]-1): 100 | pear.append(pearsonr(corr[i], corr[i+1])) 101 | pear.append(pearsonr(corr[-1], corr[0])) 102 | pear = np.array(pear) 103 | pear_1 = np.concatenate((pear[:,0].reshape(pear.shape[0], 1), (ll_con_cat_11[:, (ll_con_cat_11.shape[1]-1)]).reshape(ll_con_cat_11.shape[0],1)), axis = 1) 104 | #in above line,in pear_1 we concatenated the pear list and labels to ease the back-track 105 | 106 | #now putting a threshold on the correlation values stored in pear. Those above threshold are 107 | #stored in pear_2 list as it is and rest all other are stored as 0(zero) in pear_2 108 | #this is done so that the dimension remains same and labels remain unaffected 109 | pear_2 = np.zeros((pear_1.shape[0],2 )) 110 | for i in range(pear_1.shape[0]): 111 | if(pear_1[i][0] > 0.99995): 112 | pear_2[i] = pear_1[i] 113 | 114 | #now putting threshold on pear_2 values based on the distance factor & storing in pear_3. Those above threshold are 115 | #stored in pear_2 list as it is and rest all other are stored as 0(zero) in pear_2 116 | #this is done so that the dimension remains same and labels remain unaffected 117 | pear_3 = np.zeros((pear_2.shape[0],2)) 118 | for i in range(pear_2.shape[0]-1): 119 | if(pear_2[i][0] != 0) and (pear_2[i+1][1] != 0): 120 | indx = np.abs(((pear_2[i][1]) - pear_2[i+1][1])/int(pear_2[i][1])) 121 | if( indx >0.7 and indx < 0.99): #(pear_1.shape[0]) > 0.40): 122 | pear_3[i] = pear_2[i]#np.abs((pear_2[i][1]) - pear_2[i+1][1])/int(pear_1.shape[0])# 123 | pear_2 = pear_3 124 | 125 | 126 | #now backtracking the values found to the actual points or pixels in the actual image 127 | back_track = np.zeros((win_data.shape)) 128 | for i in range(pear_2.shape[0]): 129 | if(pear_2[i][1] != 0 ): 130 | c = int(pear_2[i][1])-1 131 | back_track[c] = win_data[c] 132 | 133 | for_index = [] 134 | for i in range(back_track.shape[0]): 135 | if(back_track[i][1] != 0): 136 | for_index.append(i) 137 | for_index = np.array(for_index) 138 | 139 | #making rectangles around the pixels or points in the image that are found after back tracking- 140 | rect=np.zeros_like(image.shape) 141 | rect=image 142 | did = int(np.sqrt(win_data.shape[0])) 143 | for i in range(for_index.shape[0]): 144 | a1_x=((for_index[i]-1)%did)*5 145 | a1_y=(int((for_index[i]-1)/did))*5 146 | a1_xe=a1_x + 10 147 | a1_ye=a1_y + 10 148 | rect=cv2.rectangle(rect,(a1_x,a1_y),(a1_xe,a1_ye),(0,255,0)) 149 | plt.imshow(rect, cmap = 'gray') 150 | plt.show() 151 | 152 | -------------------------------------------------------------------------------- /sort.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | def lab_sort(arr, aa): 4 | sortt = [] 5 | for j in range(0, aa): 6 | for i in range(arr.shape[0]): 7 | if(int(j) == int(arr[i][0])): 8 | sortt.append(arr[i]) 9 | return np.array(sortt) 10 | 11 | def f_sort(arr1, aa): 12 | final_sort = [] 13 | # cc = [] 14 | for i in range(0,aa): 15 | for_s = [] 16 | for j in range(arr1.shape[0]): 17 | if(i == arr1[j][0]): 18 | for_s.append(arr1[j][-2]) 19 | for_ss = np.array(for_s) 20 | sortted_for_s = np.sort(for_ss) 21 | for k in range(sortted_for_s.shape[0]): 22 | count = 0 23 | for l in range(arr1.shape[0]): 24 | if(arr1[l][-2] == sortted_for_s[k]) and count<1: 25 | final_sort.append(arr1[l]) 26 | count = count+1 #using count so that redundant sum values don't get repeated 27 | return np.array(final_sort) 28 | 29 | --------------------------------------------------------------------------------