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.
Recently, Yang @cite_25 proposed a copy-move forgery detection based on a modified SIFT-based detector. Wang @cite_0 presented a novel block-based robust copy-move forgery detection approach using invariant quaternion exponent moments and the falsely matched block pairs are removed by customizing the random sample consensus with QEMs magnitudes differences. It is robust to handle noise addition, lossy compression, scaling, and rotation, when compared to conventional copy-move forgeries detection techniques.
@cite_0 @cite_25
2022436565 2540421039 2149073238 2106608297
A novel copy–move forgery detection scheme using adaptive oversegmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. First, the proposed adaptive oversegmentation algorithm segments the host image into nonoverlapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions. Finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the proposed copy–move forgery detection scheme can achieve much better detection results even under various challenging conditions compared with the existing state-of-the-art copy–move forgery detection methods. 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. 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. This paper proposes a novel geometric distortion resilient image copy detection scheme based on Scale Invariant Feature Transform (SIFT) detector. By using the SIFT detector, the proposed copy detection scheme first construct a series of robust, homogenous, and larger size circular patches. And then, the cirque track division strategy and ordinal measure concept are introduced to generate a cirque-based ordinal measure feature vector for each circular patch. Besides, the ROC graph and MAP probability are utilized to estimate the two parameters (vector dimension and detection threshold) respectively. Experimental results and the related analysis show that the proposed scheme is robust to most of geometric and photometric distortions.