SMDAF: A novel keypoint based method for copy‐move forgery detection

IET Image ProcessingVolume 16, Issue 13 p. 3589-3602 ORIGINAL RESEARCHOpen Access SMDAF: A novel keypoint based method for copy-move forgery detection Guangyu Yue, Guangyu Yue orcid.org/0000-0003-4663-962X National Pilot School of Software, Yunnan University, Kuming, China Engineering Research Center of Cyberspace, Yunnan University, Kuming, ChinaSearch for more papers by this authorQing Duan, Corresponding Author Qing Duan [email protected] National Pilot School of Software, Yunnan University, Kuming, China Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kuming, China Correspondence Qing Duan, National Pilot School of Software, Yunnan University, Kuming 650000, China. Email: [email protected]Search for more papers by this authorRenyang Liu, Renyang Liu orcid.org/0000-0002-7121-1257 Engineering Research Center of Cyberspace, Yunnan University, Kuming, China School of Information Science and Engineering, Yunnan University, Kuming, ChinaSearch for more papers by this authorWenyu Peng, Wenyu Peng National Pilot School of Software, Yunnan University, Kuming, China Engineering Research Center of Cyberspace, Yunnan University, Kuming, ChinaSearch for more papers by this authorYun Liao, Yun Liao National Pilot School of Software, Yunnan University, Kuming, China Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kuming, ChinaSearch for more papers by this authorJunhui Liu, Junhui Liu orcid.org/0000-0003-3230-5653 National Pilot School of Software, Yunnan University, Kuming, China Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kuming, ChinaSearch for more papers by this author Guangyu Yue, Guangyu Yue orcid.org/0000-0003-4663-962X National Pilot School of Software, Yunnan University, Kuming, China Engineering Research Center of Cyberspace, Yunnan University, Kuming, ChinaSearch for more papers by this authorQing Duan, Corresponding Author Qing Duan [email protected] National Pilot School of Software, Yunnan University, Kuming, China Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kuming, China Correspondence Qing Duan, National Pilot School of Software, Yunnan University, Kuming 650000, China. Email: [email protected]Search for more papers by this authorRenyang Liu, Renyang Liu orcid.org/0000-0002-7121-1257 Engineering Research Center of Cyberspace, Yunnan University, Kuming, China School of Information Science and Engineering, Yunnan University, Kuming, ChinaSearch for more papers by this authorWenyu Peng, Wenyu Peng National Pilot School of Software, Yunnan University, Kuming, China Engineering Research Center of Cyberspace, Yunnan University, Kuming, ChinaSearch for more papers by this authorYun Liao, Yun Liao National Pilot School of Software, Yunnan University, Kuming, China Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kuming, ChinaSearch for more papers by this authorJunhui Liu, Junhui Liu orcid.org/0000-0003-3230-5653 National Pilot School of Software, Yunnan University, Kuming, China Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kuming, ChinaSearch for more papers by this author First published: 02 August 2022 https://doi.org/10.1049/ipr2.12578AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. Although many copy-move forgery detection (CMFD) methods have been proposed, the most existing CMFD methods are short of adaptability in detecting images, which leads to the limitation on detection effects. To solve this problem, the paper proposes a novel keypoint-based CMFD method: second-keypoint matching and double adaptive filtering (SMDAF). Motivated by image matching based on keypoint, the second-keypoint matching method is designed to match keypoints extracted from copy–move forgery images, which can be used for both the single-CMFD and the multiple-CMFD. Then, a double adaptive filter (DAF) based on the AdaLAM algorithm and the KANN-DBSCAN clustering algorithm to filter wrong keypoint matches adaptively are proposed, according to the distinct distribution of keypoints in each image. Finally, the forgery regions are presented by finding their convex hulls and padding them. Compared with existing methods, extensive experiments show that the SMDAF method significantly provides more efficiency in detecting images under simulated real-world conditions, has better robustness when facing images with different post-treatment attacks, and is more effective in distinguishing images that look copy–move forged but are real. 1 INTRODUCTION The expanding and flourishing of IoT (Internet of things) has brought us into the era of big data. It is no doubt that different kinds of multimedia data are produced, transferred, and modified each day. People are not unusual to observe that powerful image editing tools can easily manipulate a certain image [1, 2] so as for beautification and entertainment. However, this may bring potentially severe consequences for misleading personal judgment, imposing negative impacts on society and disturbing digital forensics [3, 4]. Therefore, image forgeries [5-7], including splicing, retouching, and copy–move, has drawn broad concern in several essential image application fields. Compared with other image forgeries, copy–move forgery is one of the most common manners, which copy certain areas and paste them into other parts in the same image, as is shown in Figure 1. Since the clone region with any shape can be located at any region, it is infeasible to search for all possible forgery parts. In addition, it is uneasy to detect by looking for feature differences between forged and other areas because the copy–pasted area comes from the same image, where the features (e.g. colour and noise) are compatible. Hence, there are still remaining challenges to perform copy–move forgery detection [8-10] (CMFD). In general, there are two goals for CMFD: (1) binary classification: to judge whether an image is forged or not. (2) forgery locating: to precisely label or locate elaborate designed copy–move parts from the whole image. FIGURE 1Open in figure viewerPowerPoint From left to right: original image (a), copy–move forged image (b), and binary groundtruth overlying copy–move area (c) In the past few years, many copy–move forgery detection methods have been proposed, which can be roughly divided into block-based methods [11-15], deep learning-based methods [16-21], and feature point-based methods [22-30]. The block-based algorithms are inefficient and do not perform well in detecting the copy–move regions with different geometric and post-processing attack operations. Although deep learning-based methods have gained state-of-the-art success in many other fields, one interesting fact here is that these methods need many training data, and they cannot achieve good performance in CMFD. Compared with block-based and deep learning-based methods, the keypoint-based methods have achieved relatively superior performance concerning detection consequences and running speed [29, 31]. However, in the existing keypoint-based methods, the descriptors extracted from keypoints may not all meet the needs. The results contain a large number of false matching and reduce the matching accuracy. To overcome this weakness, researchers proposed some algorithms for filtering the wrong matching keypoint pairs [27-29]. Chen et al. [27] aggregated the feature and colour of the extracted keypoints, then used reverse-G2NN and outpoint filtering algorithm to filter the keypoints. Wang et al. [28] used candidate clustering based on offset information to remove wrong matching pairs and used RANSAC and ZNCC to locate duplicate regions. And Yang et al. [29] proposed a two-stage(mesh-based and cluster-based) filter to select most of the keypoint pairs of wrong matching and then applied the Delaunay triangulation algorithm for image matting. The results are still not satisfying because they often use a general model to filter different keypoint sets. However, these methods are lack of adaptability because the keypoint set extracted from each image has its distinctive distribution, which leads to the poor effect when filtering some special sets. To solve this problem, different from the works mentioned above, we propose a novel copy–move forgery detection method called SMDAF, based on second-keypoint matching (SM) and double adaptive filtering (DAF). The main contributions of this paper are as follows: This paper proposes a novel keypoint-based method for CMFD called SMDAF. It is more adaptable to filter false keypoint matching and the locating of the forgery areas is more accurate. The proposed second-keypoint matching method (SM) is used to obtain more keypoint matching, which can be used for both single and multiple CMFD problems. The proposed double adaptive filter (DAF) shows effective adaptability in filtering false keypoint matching and clustering internal keypoints. It also makes the location of the forgery areas more accurate. Extensive experiments show that the SMDAF method is significantly more efficient in detecting images under simulated real-world conditions, has better robustness against images with different post-processing attacks, and is more effective in discriminating images that look copy–move forged but are real. The rest of this article is organised as follows: The related work is given in Section 2, the proposed method is explained in Section 3, the experiments shown in Section 4, and the whole paper is concluded in Section 5. 2 RELATED WORK Copy–move forgery detection methods can be roughly divided into block-based methods, deep learning-based methods and keypoint-based methods. In this section, we introduce related researches in these areas. Block-based methods Block-based methods divide the image into overlapping blocks and map them based on their features. Different methods use different transformations to represent the features of blocks. In ref. [11], Ouyang et al. proposed a method based on the pyramid model and Zernike moments. In ref. [12], Mahmood et al. combined DWT and Hu Moments together. Warif et al. proposed CMF-iteMS that extracted feature by using polar cosine transform (PCT) [13]. Li et al. used polar sine transform (PST) to tramsform features of blocks. And Emam et al. used the polar complex exponential transform (PCET) [15] kernels to represent each block. However, high computational complexity is the common defect of the above methods, and it is not suitable for solving large-scale rotation scaling and typical image processing operations like noise. Deep learning-based methods In recent years, some deep learning-based methods have been applied to solve copy–move forgery detection problems and proved to have great potential based on their excellent performance. Wu et al. [17] proposed an end-to-end deep neural network to detect copy–move forgery. In ref. [16], they improved their work as BusterNet with dual-branch architecture. Then, in ref. [19], Zhong et al. proposed the end-to-end Dense-InceptionNet based on the current popular convolutional neural network. In ref. [18], Zhu et al. developed AR-Net based on adaptive attention and residual refinement. Different from the above methods that are concentrate on CMFD, Abhishek et al. [20] used a deep convolutional neural network and semantic segmentation to detect copy–move and splicing forged images, and Yohanna et al. [21] analysed two deep learning methods, explored the detecting effect of using different neural network structures. However, these deep learning-based methods generally require a fixed image size and a vast number of training and test data. Therefore, the detection results of deep learning-based methods entirely depend on the training data's quality. They failed to detect copy–move forgery images when faced with unpredictable characteristics generated in a new way. In short, the existing methods based on deep learning are still not well perform for copy–move forgery detection. Keypoint-based methods Some researchers use the local invariance technology for image forensics, such as scale-invariant feature transform (SIFT) [22-25, 27, 32], speeded-up robust features (SURF) [26, 33] and oriented fast and rotated brief (ORB) [34, 35]. Amerini et al. [22] and Pan et al.[23] proposed methods based on SIFT for image copy–move forgery detection and achieved a satisfying solution. Dhivya s et al. [26] used SURF to extract features and then trained the SVM for classification. Moreover, Tian et al. [35] used ORB to extract features that can significantly lessen detection time. For improving the number of keypoints, Yang et al. [24] proposed an algorithm based on SIFT that is robust to image transform to extract keypoints from images. In ref. [25], Li et al. discovered that changing the image contrast and scaling the image can increase the number of SIFT feature points, based on this, they optimised the performance of small or smooth copy–move areas in their method. However, due to the limitation of descriptors extracted from keypoints, they often return false matches, which affects the detecting results. To improve the effects of CMFD, we propose a novel method called SMDAF, which can filter false matches effectively and locate the forgery areas correctly. Experimental results show that SMDAF has competitive results compared with the existing methods. 3 PROPOSED METHOD In this section, we first briefly introduce the related concepts and fundamental knowledge for a better understanding of our later analysis. Then we present a step by step description of our method. As shown in Figure 2, the method can be divided into the following four steps: keypoint extraction, second-keypoint matching, double adaptive filtering, and forgery locating. FIGURE 2Open in figure viewerPowerPoint Framework of SMDAF. In the first step, the SIFT algorithm is used to extract keypoints for a given original image. To gain more keypoints, we extract them from both the original and the magnified for each image. In the second step, we copy the keypoint set containing descriptors extracted in step 1 and input them into the proposed second-keypoint matching method. As shown in step 3, we use AdaLAM as the first outpoint filter; after aggregating and de-duplicating the output, we get filtered discrete points (DP). For the unique filtered set generated from each image, we use the modified KANN-DBSCAN algorithm called Multi-DBSCAN to find the most suitable hyperparameters(i.e. Eps and Minpts) and execute DBSCAN clustering. After that, we filter out the clusters whose results are labelled as noise or the clusters with too few points. In step 4, we finally got the mask, which covered the copy–move part, by finding the convex hulls of remained clusters and padding them Preliminaries 3.1.1 AdaLAM outpoint filter As a fast and accurate outlier filter, AdaLAM is mainly used in image matching [36] based on keypoint. The methods in keypoint-based image matching often form the nearest neighbour matching set to draw correspondences between images, as is shown in Figure 3(a). The main steps of AdaLAM algorithm can be summarised as follows: (1) It takes a large number of matched keypoints as input, then selects a limited number of seed points that are confident and well-distributed based on neighbouring compatible correspondences in this algorithm. (2) By running highly parallel RANSAC with sample-adaptive inlier thresholds, it verifies local affine consistency in neighbourhoods of each seed point. (3) It outputs the union of all the inliers of the seed points with strong enough support within each one's specific inlier threshold. FIGURE 3Open in figure viewerPowerPoint Typical examples of image matching (a) and copy–move forgery detection (b) Similar to image matching, copy–move forgery detection based on keypoint correspond matchings in one image, as is shown in Figure 3(b). Motivated by this, our proposed method uses the AdaLAM algorithm for first outpoint filtering. 3.1.2 KANN-DBSCAN The KANN-DBSCAN algorithm is based on DBSCAN [37], a density-based clustering method that is sensitive to Eps and MinPts. Each parameter can be explained in Figure 4. FIGURE 4Open in figure viewerPowerPoint Explain of parameters in DBSCAN clustering. The circle represents ε-neighbourhood, which is limited by its radius Eps. The red dot ensures that the points in the circle exceed MinPts, the minimum number of points that can be chosen as a seed in the ε-neighbourhood. These dots are connected by green lines, meaning they are in the same point set To adaptively find the optimum parameter pair (Eps, MinPts) that can be set for getting a better clustering effect, the KANN-DBSCAN algorithm is proposed. It first generates the candidate Eps list by using the proposed K-ANN based on the average nearest neighbour algorithm [38, 39]. As each Eps correspondings many MinPts, the algorithm calculates the mathematical expectation of them to get the MinPts list. Then it combines the Eps list and the Minpts list to a parameter pair list, successively sets them in pairs as the parameters of DBSCAN, and observe the number of clusters. Once the number remains the same after setting three consecutive parameter pairs, the algorithm records it as the best called N. After that, it continues DBSCAN clustering until the cluster number changed, and the last pair of parameters ( Eps best $\text{Eps}_{\text{best}}$ , MinPts best $\text{MinPts}_{\text{best}}$ ) setted before changing is the optimal. Keypoint extraction We use SIFT to extract the keypoints. Then, to provide enough basic keypoints, as Li [25] mentioned, we extract and combine the keypoint sets ( KP o $\text{KP}_o$ and KP z $\text{KP}_z$ ) from the original image and the scaled image separately, to obtain the union set called KP $\text{KP}$ . Finally, for each keypoint P j $P_j$ in the keypoint set KP $\text{KP}$ , where j ∈ {1, 2..., s}, we extract the local binary pattern features, which weight are rotation invariance and uniformity, to form the 132-dimensional keypoint feature descriptors. The details are shown in Formula (1) and Formula (2), where S is the number of keypoints, and j ∈ (0, S): KP = { P 1 , P 2 , P 3 , … , P S } \begin{align} & \text{KP}= \lbrace P_1, P_2, P_3, \ldots , P_{\text S} \rbrace \end{align} (1) P j = { P T j , A j , S j , D j } \begin{align} & P_j = \lbrace PT_j, A_j, S_j, D_j\rbrace \end{align} (2) In Formula (2), PT j $\text{PT}_j$ is a 1 × 2 matrix, indicating 2D coordinates. A j $A_j$ is a 1 × 1 matrix representing the gradient direction, which is calculated by SIFT around the neighbourhood of keypoints. S j $S_j$ is a 1 × 1 matrix means the importance of the keypoint. Finally, D j $D_j$ is a 1 × 128 eigenvector calculated by SIFT. Second-keypoint matching After the previous step, the image features are extracted as keypoints. Inspired by image matching based on keypoint, we proposed the second-keypoint matching method. It matches two keypoint sets from identical images: for each keypoint in the first set, we match the second similar keypoint corresponding to another set. To make the matching method suitable for CMFD, which is divided into the single-CMFD and the multiple-CMFD, we analysed the distribution of keypoints and found that regions with dense keypoints often cover the copy–move areas regardless of single or multi copy–move forgery. The reason can be explained like this: As the copy–pasted regions must be more similar than other unforged regions if we ignore distinguishing them, the feature descriptors of the keypoints extracted from these regions must be very close. As a result, for a keypoint extracted from a random copy–move region, we can always find another keypoint closest to the current in other copy–move areas. This "another keypoint" is embodied as the second similar in the proposed method. For single-CMFD, as shown in Figure 5, comparing the similarity by feature descriptors, the point P 0 R $P^{\text R}_0$ in the right picture is the most similar to the current point P 0 L $ P^{\text L}_0$ in the left picture, as shown by the red line. However, it is not suitable for CMFD because the descriptors of P 0 L $ P^{\text L}_0$ and P 0 R $P^{\text R}_0$ must be the same. Since the points extracted by SIFT are not sensitive to various post-processing, that is to say, the descriptors corresponding to the point after post-processing transformation are very close to the original point's descriptors. Hence, the second similar descriptor mentioned above has a high probability of being the copy-moving part's corresponding point. In our work, we match such a second similar keypoint corresponding to the current point from the right picture, and the matching results are P 0 L $ P^{\text L}_0$ and P 1 R $ P^{\text R}_1$ . FIGURE 5Open in figure viewerPowerPoint Keypoint matching for single-CMFD Things are different for multiple-CMFD, the most common situation in the CMFD task. It takes multiple forgeries from the same source region. As is shown in Figure 6, we use S x y $ S_x^y$ to represent copy–move areas, where the subscript x can be 0, 1 and 2 to represent a certain area separately, and the superscript L $ \text{L}$ or R $ \text{R}$ denotes the left image or right image. A keypoint P is selected in each S x y $ S_x^y$ , which superscript and subscript representations are the same as S x y $ S_x^y$ . Unlike the single one, there are two following situations for matching keypoints in multi-copy–move forgery: (a) P 0 L $P^{\text L}_0$ → P 1 R $P^{\text R}_1$ : P 0 L $P^{\text L}_0$ match P 1 R $P^{\text R}_1$ as the second similar keypoint, but not vice versa. (b) P 0 L $P^{\text L}_0$ ↔ P 1 R $P^{\text R}_1$ : The two keypoints match with each other. To detect multiple-CMFD, in our work, we regard both (a) and (b) as matching and record all corresponding points. FIGURE 6Open in figure viewerPowerPoint Keypoint matching for multiple-CMFD Double adaptive filtering Unlike the other CMFD works, we use AdaLAM for first adaptive outpoint filtering, separating each copy–move region by clustering discrete points and refilter by deleting unsatisfied clusters. 3.4.1 First adaptive filtering For the first adaptive outpoints filtering, as shown in Figure 7, we first take a comprehensive set of hypothesis second matches from the two similar keypoint sets as input, and each match is represented as the yellow line in Figure 7(a). Then select the reasonable propagation hypothesis corresponding to the rough areas displayed as blue circles in Figure 7(b). As shown in Figure 7(c), for the set of all hypothesis matches, each considered region is consistent with the corresponding hypothesis of the same region. Moreover, we only retain the locally consistent correspondence with sufficient support for affine transformation, and the results can be seen in Figure 7(d). FIGURE 7Open in figure viewerPowerPoint The procession of first adaptive outpoints filtering As the method outputs two discrete point sets of coordinates, we overlap the two images to unify the coordinate values. Shown in Figure 8, the blue dots represent the points in copy–move regions, and the red points indicate the outliers to be filtered. After de-duplicate, we finally get a set of discrete points. FIGURE 8Open in figure viewerPowerPoint Overlap after first filtering 3.4.2 Adaptive clustering The output of the step 2 is a set of discrete points called DP. As is shown in Figure 9, the red dots indicates DP. There is an interesting phenomenon discovered that these points in DP are concentrated, and they exactly cover the whole copy–move region of an image. Based on this, we intend to separate each copy–move region by clustering discrete points. An easy way to realise this is by K-means [40]. However, the K-means needs to specify the number of clustering K, as the number of image copy–move regions in CMFD cannot be fixed entirely. Inspired by the hotspot location mining based on given coordinate's density in the data mining field, we use DBSCAN to cluster discrete points. This algorithm can divide the current discrete points into flexible standard clusters (marked 1,2,3...) and a noise cluster (marked −1). FIGURE 9Open in figure viewerPowerPoint The image marked with DP Since each image has its most suitable clustering pattern, referring to the KANN-DBSCAN algorithm that is introduced in Section 3.1.2, we modify the part that generates the candidate Eps and Minpts lists, develop Multi-DBSCAN to find the hyperparameter pair list called H, which can be written as H = { ( Eps 1 , MinPts 1 ) , ( Eps 2 , MinPts 2 ) … … ( Eps L , MinPts L ) } \begin{align} H = \lbrace (\text{Eps}_1,\text{MinPts}_1),(\text{Eps}_2,\text{MinPts}_2)\ldots\ldots\nonumber \\ (\text{Eps}_{\text L},\text{MinPts}_{\text L})\rbrace \end{align} (3) For the reason that the algorithm needs to give the range of cluster numbers, we set the range to be 2–10 after observing various datasets. Then, we find all hyperparametric cases as candidates that can achieve 2–10 clustering, arranged from small to large. Next, we extract the candidate Eps ada $\text{Eps}_{\text{ada}}$ items and MinPts ada $\text{MinPts}_{\text{ada}}$ items from the H and average them to obtain the adaptive hyperparameters. Mathematically, Eps ada = ∑ n = 1 L I Eps n L \begin{align} & \text{Eps}_{\text{ada}} = \frac{\sum _{n=1}^{\text L}I_{\text{Eps}}^n}{L}\end{align} (4) MinPts ada = ∑ n = 1 L I MinPts n L \begin{align} & \text{MinPts}_{\text{ada}} = \frac{\sum _{n=1}^{\text L}I_{\text{MinPts}}^n}{L} \end{align} (5) It is straightforward but practical to specify the unique hyperparameter pair and the most likely cluster number during average because the average result is bound to be biased towards a cluster with the best parameter pairs in H. Finally, we set the obtained parameters for DBSCAN clustering. 3.4.3 Refiltering In this subsection, we find the convex hulls [41] of clusters in Section 3.4.2 to help us refine the results. Take Figure 10 for an example: the red polygon is called the convex hull, when it wraps all the points and ensures it is convex. FIGURE 10Open in figure viewerPowerPoint The illustration of convex hull All know that the clusters should be filtered if they cannot satisfy the criteria, so it is needed to re-filter these clusters in Section 3.4.2. There are two following situations: One is the cluster composed of less than three points, and the other is that the whole points in the cluster are collinear. In addition, we also need to filter the lousy cluster results treated as noises, which usually contain the outpoints that were not filtered out. Based on these preliminaries above, we find the convex hulls and refiltering the unsatisfied. Figure 11 shows an experiment comparing the effects of RANSAC, first adaptive filtering, and double adaptive filtering. As shown in Figure 11, after applying RANSAC, noticeably, too few keypoints are left (column (b)).The first adaptive filtering procedure removes most false-matching pairs but remains some wrong keypoints (column (c)).We do not care about matching pairs during refiltering but the distribution of keypoints. We gather all the points together, cluster them, and then filter the unsatisfied clusters. Finally, the proposed filter removes all false keypoints in this experiment (column (d)). FIGURE 11Open in figure viewerPowerPoint Comparing the filtering results of different filters. (a) Forgery image; (b) RANSAC filtering; (c) only first adaptive filtering; (d) double adaptive filtering Forgery locating To locate the copy–move forged areas properly, we directly fill the convex hull areas to cover the copy–move areas. The results are illustrated in Figure 13, Figures 15 and 16. These figures display the predicted area's superposition and masks at the pixel level. The meaning of colours are as follows: background (blue areas), correctly detected pixels (green areas), undetected pixels (red areas), and falsely detected pixels (yellow areas). The reason is that the convex hull of the point set will also wholly draw out the edge area of the current point set, which can cover the copy–move forged area. FIGURE 12Open in figure viewerPowerPoint Ablation experiments using CoMoFoD dataset. Showing curves of Precision $\text{Precision}$ , Recall $\text{Recall}$ , and F1. FIGURE 13Open in figure viewerPowerPoint Examples from pixel-level ablation experiments FIGURE 14Open in figure viewerPowerPoint The F1 curves using different algorithms with the CoMoFoD dataset FIGURE 15Open in figure viewerPowerPoint Examples of using different algorithms to predict the post-processing attack image in the CoMoFoD dataset. From top to bottom: (a) forged images. (b) BusterNet [16]. (c) DOA-GAN [46]. (d) HFPM [25]. (e) SMDAF-S