Frame duplication is to duplicate a sequence of consecutive frames and insert or replace to conceal or imitate a specific event content in the same source video. To automatically detect the duplicated frames in a manipulated video, we propose a coarse-to-fine deep convolutional neural network framework to detect and localize the frame duplications. We first run an I3D network to obtain the most candidate duplicated frame sequences and selected frame sequences, and then run a Siamese network with ResNet network to identify each pair of a duplicated frame and the corresponding selected frame. We also propose a heuristic strategy to formulate the video-level score. We then apply our inconsistency detector fine-tuned on the I3D network to distinguish duplicated frames from selected frames. With the experimental evaluation conducted on two video datasets, we strongly demonstrate that our proposed method outperforms the current state-of-the-art methods.
In this paper we propose a robust and fully automatic method to detect duplicated regions in digital images. Copied areas in uniform and non-uniform regions are detectable in our method. There are several methods to make forged images, but the most common is copy-move forgery, that the forger copies a part(s) of image and pasted it into another part(s) of that image. Many researchers have done beneficial researches on it. Most of them can find only copy-moved forgery. In other words, they can find regions which are only copied and pasted without any changes, but failed to find copied regions with scaling or rotating before pasting. Many forgers make some changes on copied regions so that the image sounds more natural. SIFT features can find forged regions even if they are rotated or scaled. This method has some other advantages, but failed to find flat copied regions. Zernike moments are invariant against rotation. They can find flat copied regions too, but sensitive to scaling. So it is clear that using these two features is very proper to detect all copied regions in an image. By applying SIFT detection method on overall the image and then Zernike moments detection method on regions where SIFT features have not been found, The processing time is reduced in comparison to applying each of these two methods on entire image. Region duplication forgery, in which a part of a digital image is copied and then pasted to another portion of the same image, is one of the simple and common image forgery techniques. Most of the existing algorithms are not robust to the post region duplication image processing, and have high time complexity. In this paper, we describe an e-cient and robust algorithm for detecting and localizing this type of malicious tampering. The image is flrst reduced in dimension by Gaussian pyramid, and the Hu moment is applied to the flxed sized overlapping blocks of low-frequency image. The eigenvectors are lexicographically sorted. Then, similar eigenvectors are matched by a certain threshold value. Finally, the area threshold value is proposed to remove the wrong similar blocks. The mathematical morphology operations are performed to locate the tampered part. Experimental results show that our method is robust and that it can not only successfully detect this type of tampering for images subject to various forms of post region duplication image processing, including noise contamination, blurring, and severe lossy compression, but also reduce the total number of blocks to narrow block-matching searching space, which can improve the method e-ciency. A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.