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.
Copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it @cite_3 . Different methodologies have high reckoning time and not suitable for ongoing applications, for example, PCA, DWT, or SVD. For example, Wang @cite_18 propose a dimensionality diminishment based system and utilizes PCA (Principal Component Analysis) on the different pieces in a substitute mode. The drawback is that for dark scale pictures furthermore forms each shading direct in shading pictures and PCA is for recognition the fakes. Mohamadian @cite_5 develop a Singular Value Decomposition (SVD) based method in which the image is isolated into numerous little covering squares and after that SVD is requested to remove the copied frames. Its shortcoming is that the method is not for shading pictures.
@cite_5 @cite_18 @cite_3
2149073238 2570989757 2540421039 2020132033
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. A very common way of image tampering is the copy-move attack. When creating a copy-move forgery, it is often necessary to add or remove important objects from an image. To carry out forensic analysis of such images, various copy-move forgery detection (CMFD) methods have been developed in the literatures. In recent years, many feature-based CMFD approaches have emerged due to its excellent robustness to various transformations. However there is still place to improve performance further. Many of them would suffer from the problem of insufficient matched key-points while performing on the mirror transformed forgeries. Furthermore, many feature-based methods might hardly expose the tempering when the forged region is of uniform texture. In this paper, a novel feature-based CMFD method is proposed. Key-points are detected by using a modified SIFT-based detector. A novel key-points distribution strategy is developed for interspersing the key-points evenly throughout an image. Finally, key-points are descripted by an improved SIFT descriptor which is enhanced for the CMFD scenario. Extensive experimental results are presented to confirm the efficacy. The detection of forgeries in color images is a very important topic in forensic science. Copy–move (or copy–paste) forgery is the most common form of tampering associated with color images. Conventional copy–move forgeries detection techniques usually suffer from the problems of false positives and susceptibility to many signal processing operations. It is a challenging work to design a robust copy–move forgery detection method. In this paper, we present a novel block-based robust copy–move forgery detection approach using invariant quaternion exponent moments (QEMs). Firstly, original tempered color image is preprocessed with Gaussian low-pass filter, and the filtered color image is divided into overlapping circular blocks. Then, the accurate and robust feature descriptor, QEMs modulus, is extracted from color image block holistically as a vector field. Finally, exact Euclidean locality sensitive hashing is utilized to find rapidly the matching blocks, and the falsely matched block pairs are removed by customizing the random sample consensus with QEMs magnitudes differences. Extensive experimental results show the efficacy of the newly proposed approach in detecting copy–paste forgeries under various challenging conditions, such as noise addition, lossy compression, scaling, and rotation. We obtain the average forgery detection accuracy (F-measure) in excess of 96 and 88 across postprocessing operations, at image level and at pixel level, respectively. Copy-move forgery is one of the most common type of tampering in digital images. Copy-moves are parts of the image that are copied and pasted onto another part of the same image. Detection methods in general use block-matching methods, which first divide the image into overlapping blocks and then extract features from each block, assuming similar blocks will yield similar features. In this paper we present a block-based approach which exploits texture as feature to be extracted from blocks. Our goal is to study if texture is well suited for the specific application, and to compare performance of several texture descriptors. Tests have been made on both uncompressed and JPEG compressed images.