Video copy-move forgery detection is one of the hot topics in multimedia forensics to protect digital videos from malicious use. Several approaches have been presented through analyzing the side effect caused by copy–move operation. However, based on multiple similarity calculations or unstable image features, a few can well balance the detection efficiency, robustness, and applicability. In this paper, we propose a novel approach to detect frame copy–move forgeries in consideration of the three requirements. A coarse-to-fine detection strategy based on optical flow (OF) and stable parameters is designed. Specifically, coarse detection analyzes OF sum consistency to find suspected tampered points. Fine detection is then conducted for precise location of forgery, including duplicated frame pairs matching based on OF correlation and validation checks to further reduce the false detections. Experimental evaluation on three public video data sets shows that the proposed approach is effective and efficient in detecting both unsmooth manipulation and common smooth forgery and also with high robustness to regular attacks, including additive noise, filtering, and compression.

Coarse-to-Fine Copy-Move Forgery Detection for Video Forensics

Digital videos are one of the most widespread forms of multimedia in day to day life. These are widely transferred over social networking websites such as Facebook, Instagram, WhatsApp, YouTube, etc. through the Internet. Availability of modern and easy to use editing tools have facilitated the modification of the contents of the digital videos. Therefore, it has become an essential concern for the legitimacy, trustworthiness, and authenticity of these digital videos. Digital video forgery detection aims to identify the manipulations in the video and to check its authenticity. These techniques can be divided into active and passive techniques. In this paper, a comprehensive survey on video forgery detection using passive techniques have been presented. The primary goal of this survey is to study and analyze the existing passive video forgery detection techniques. Firstly, the preliminary information required for understanding video forgery detection is presented. Later, a brief survey of existing passive video forgery detection techniques based on the features, forgery identified, datasets used, and performance parameters detail along with their limitations are reviewed. Then, anti-forensics strategy and deepfake detection in the video are discussed. After that, standard benchmark video forgery datasets and the generalized architecture for passive video forgery detection techniques are discussed. Finally, few open challenges in the field of passive video forgery detection are also described.

A comprehensive survey on passive techniques for digital video forgery detection

Frame insertion, deletion and duplication are common inter-frame tampering operations in digital videos. In this paper, based on similarity analysis, a passive-blind forensics scheme for video shots is proposed to detect inter-frame forgeries. This method is composed of two parts: HSV (Hue-Saturation-Value) color histogram comparison and SURF (Speeded Up Robust Features) feature extraction together with FLANN (Fast Library for Approximate Nearest Neighbors) matching for double-checking. We mainly calculate H-S and S-V color histograms of every frame in a video shot and compare the similarity between histograms to detect and locate tampered frames in the shot. Then we utilize SURF feature extraction and FLANN matching to further confirm the forgery types in the tampered locations. Experimental results demonstrate that the proposed detection method is efficient and accurate in terms of forgery identification and localization. In contrast to other inter-frame forgery detection methods, our scheme can detect three kinds of forgery operations and has its own superiority and applicability as a passive-blind detection method.

Inter-frame passive-blind forgery detection for video shot based on similarity analysis

In the midst of low cost and easy-to-use multimedia editing software, which make it exceedingly simple to tamper with digital content, the domain of digital multimedia forensics has attained considerable significance. This research domain deals with production of tools and techniques that enable authentication of digital evidence prior to its use in various critical and consequential matters, such as politics, criminal investigations, defense planning. This paper presents a forensic scheme for detection of frame-based tampering in digital videos, especially those captured by surveillance cameras. Frame-based tampering, which involves insertion, removal or duplication of frames into or from video sequences, is usually very difficult to detect via simple visual inspection. Such forgeries, however, disturb the temporal correlation among successive frames of the tampered video. These disturbances, when analyzed in an appropriate manner, help reveal the evidence of forgery. The forensic technique presented in this paper relies on objective analysis of prediction residual and optical flow gradients for the detection of frame-based tampering in MPEG-2 and H.264 encoded videos. The proposed technique is also capable of determining the exact location of the forgery in the given video sequence. Results of extensive experimentation in diverse and realistic forensic set-ups show that the proposed technique can detect and locate tampering with an average accuracy of 83% and 80% respectively, regardless of the number of frames inserted, removed or duplicated.

Inter-frame forgery detection in H.264 videos using motion and brightness gradients

During the process of authenticating the integrity of digital videos, double compression is an important evidence. High-efficiency video coding (HEVC) is the latest coding standard proposed in 2013 and has shown superior performance over its predecessors. It brings in several novel syntactic units, such as coding tree unit (CTU), prediction unit (PU), and transform unit (TU). Few methods have been reported to detect HEVC double compression by utilizing such new characteristics. In this paper, a novel scheme based on TU is proposed to detect double compression of HEVC videos. The histogram of each TU partition type in the first I/P frame of all GOPs is calculated. The feature set is fed into SVM to classify single and double compressed videos. Experimental results have demonstrated the effectiveness of TU-based feature (accuracy in [0.84, 0.97]) that bears very low dimension (10-D). And it is also shown that TU-based feature set can be combined with other feature sets to boost their performance.

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HEVC double compression detection under different bitrates based on TU partition type

Double HEVC compression detection is of great importance in video forensics. However, effective detection algorithm based on the same Qps is rarely reported. In this paper, a novel method based on the same Qps is applied in dealing with double HEVC compression detection. Firstly, the numbers of PU blocks with the size of 4×4 in each I frame is extracted during the codec procedures when the video is compressed with the same Qps. Then, calculate the standard deviation of 4×4 PU blocks difference (SDoPU) before and after the compression. Finally, select the appropriate threshold for compression testing classification according to the SDoPU statistical feature. By performing numerical experiments, We prove that the proposed algorithm is of high classification accuracy for detecting double HEVC compression.

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Double HEVC Compression Detection with the Same QPs Based on the PU Numbers

Frame deletion and duplication are common inter-frame tampering methods in digital videos. In this paper, an efficient forensic method based on motion vector pyramid (MVP) and its variation factor (VF) is proposed to detect frame deletion and duplication in videos with static background. This method is composed of two parts: feature extraction and discontinuity point detection. In the stage of feature extraction, each frame of the video is transformed to grayscale image firstly. Then, motion vector pyramid (MVP) sequence and its corresponding variation factor (VF) are calculated for every two adjacent frames. In the stage of discontinuity point detection, forgery type is identified and tampering point is localized by performing modified generalized ESD test. Experimental results show that the proposed method is efficient at forgery identification and localization. Compared with other existing methods on inter-frame forgery detection, our proposed method is more generic.

Dong-Dong Li

Papers

Double HEVC Compression Detection with the Same QPs Based on the PU Numbers

Inter-frame Forgery Detection for Static-Background Video Based on MVP Consistency