In this paper, a semi fragile video watermarking scheme based on chromatic DCT is proposed, which can protect copyright and tamper detection simultaneously. Firstly, through the experiment, it is found that the chroma block has a more stable prediction mode relative to the luminance block, which can effectively reduce the asynchronous probability brought by the change of the prediction mode. At the same time, blocks contains more the number of nonzero (NNZ) coefficients, the less the prediction mode changes. Therefore, for each macroblock, the algorithm sort its 4 × 4 sub macroblock according to the NNZ of chroma DCT coefficients, then select the sub block which has most NNZ residual coefficients. According to the secret key K and medium frequency stability of Intra 4 × 4 coefficients, modulate the relationship of three DCT coefficients near medium frequency. Secondly, the sensitivity of the prediction mode to malicious attacks and recompression operations is different. Therefore, the algorithm classifies the prediction mode of the macroblock and generates the authentication code using the prediction mode. The experimental results show that the video quality is negligible affected after watermark embedding, and the change of video bit rate is basically constant. At the same time, the chromatic DCT algorithm can also use the authentication code of the prediction mode to detect and locate the tampering at the 4 × 4 sub block level with good robustness.

A Semi-fragile Video Watermarking Algorithm Based On Chromatic Residual DCT

Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it's vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella. 

/pdf/a-comprehensive-taxonomy-on-multimedia-video-forgery-28mdj47r.pdf

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

An Anti-Recompression Video Watermarking Algorithm in Bitstream Domain

Scalable coding methods are widely considered as promising coding approaches for image/video transmission in heterogeneous environments. To copyright protection of scalable coded videos, scalable video watermarking was introduced which enables reconstructing various scales of watermark from watermarked videos. This paper proposes a novel human vision system-based, spread spectrum method to scalable video watermarking. The host video is decomposed using newly proposed spatio-temporal motion-compensated 3D wavelet decomposition, called k(
 $$\hbox {t}+2\hbox {D}$$
 ) DWT decomposition, to frequency sub-bands. A scalable decomposition of the watermark is inserted into the entire frequency sub-bands of decomposed video. At each frequency sub-band, the watermark data are inserted into the selected coefficients of the sub-band frames in a way that the watermark embedding visual artifact occurs in the highly textured, highly contrasted, and very dark/bright areas of the video frames. The experimental results show that the watermarked test videos are highly transparent and robust against scalable video coding even at very low decoding bit-rates and some other video processing attacks. The proposed approach can guarantee copyright protection for scalable coded videos, especially over heterogeneous networks.

HVS-based scalable video watermarking

Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

Adaptive Video Watermarking against Scaling Attacks Based on Quantization Index Modulation

In this paper, we present a simple but effective video watermarking scheme robust enough against scalable lossy recompression and transcoding. Most of video watermarking algorithms use DCT or DWT, but few of those method can withstand scalable recompression or transcoding. Algorithms with DWT and DCT usually based on specific encoding format, so the watermark will be lost after transform embedded video to other encoding formats. In our method, we use scene change to choose frames to embed watermark, so it can resist attack of aimless frame dropping. We use Arnold transformation to enhance the imperceptibility of the watermark. Our watermark is a fixed-size binary image, after Arnold transformation, it is uniformly embedded to the chosen frames by a spatial random mapping algorithm. The watermark after embedding to the frame has a high imperceptibility. The proposed algorithm allows blind detection without use of origin video. The embedded watermark can be extracted in a low resolution video recompressed from an embedded high resolution video or transcoded from other format embedded video.

A robust video watermarking scheme to scalable recompression and transcoding

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A robust video watermarking scheme to scalable recompression and transcoding