AN INVESTIGATION OF DIFFERENT VIDEO WATERMARKING TECHNIQUES

Watermarking is an advanced technology that identifies to solve the problem of illegal manipulation and distribution of digital data. It is the art of hiding the copyright information into host such that the embedded data is imperceptible. The covers in the forms of digital multimedia object, namely image, audio and video. The extensive literature collected related to the performance improvement of video watermarking techniques is critically reviewed and presented in this paper. Also, comprehensive review of the literature on the evolution of various video watermarking techniques to achieve robustness and to maintain the quality of watermarked video sequences

Digital Video Watermarking Robust Against Camcorder Recording Based on DWT-SVD

In order to reduce the block effects in the dark regions and improve the flicker in the bright regions of the existing video watermark algorithms, we propose an improved video watermarking algorithm against camcorder recording based on DWT-SVD. In proposed algorithm, 3th level Discrete Wavelet Transform (DWT) is applied to Y luminance of every single frame, and Singular Value Decomposition (SVD) is used on sub-band of DWT. Watermark sequence is embedded by fine-tuning the singular value of consecutive frames. Experimental results show that the proposed algorithm is robust against many different attacks such as geometric attack, signal processing and camcorder recording. Moreover, the proposed scheme can reduce the blocks effect and improve the flicker by embedding watermark into edge feature of video frame. Although, the method can provide high video quality than the existing schemes, however, it is not robust to strong compression such as MPEG

An Improved Histogram Based Boosting Detection Rate Video Watermarking Algorithm

The existing histogram based video watermarking algorithm with temporal modulated is robust to combined attacks, but the watermark detection rate is not high due to watermark cannot embedded to the smoothness and still areas effectively. To increase the watermark detection rate, in this paper, we proposed the improved algorithm of shot segmentation first and then propose an improved video watermarking algorithm which firstly construct the watermark template in each frame video in the same shot through computing block based histogram and selecting the position of the relative high variance. Then we embed the watermark template into the video frame by temporal modulation without changing the destination of the shot group of the consecutive frames. The watermark sequence is extracted by comparing the correlation distribution of video frame and corresponding watermark template in the time domain. Experimental results demonstrate that the proposed algorithm is robust to recording attacks and guarantee the watermarking video quality at the same time, besides the watermark sequences can embedded to the smoothness and still areas effectively, and the watermark detection rate can increase by about 10% than previous methods

Recent Advances in Digital Image and Video Forensics, Anti-forensics and Counter Anti-forensics

Image and video forensics have recently gained increasing attention due to the proliferation of manipulated images and videos, especially on social media platforms, such as Twitter and Instagram, which spread disinformation and fake news. This survey explores image and video identification and forgery detection covering both manipulated digital media and generative media. However, media forgery detection techniques are susceptible to anti-forensics; on the other hand, such anti-forensics techniques can themselves be detected. We therefore further cover both anti-forensics and counter anti-forensics techniques in image and video. Finally, we conclude this survey by highlighting some open problems in this domain

Image Watermarking using Chaotic Watermark Scrambling and Perceptual Quality Evaluation

In this report, a watermarking method for grayscale images is proposed that is invisible and robust to certain attacks. Chaotic maps are used to generate the watermark which improves the security of the method. The crown watermark is embedded in the real part of DFT domain and the embedding position is determined by the SURF algorithm. The peak signal-to-noise ratio is used to evaluate the perceived quality of the marked image. Normalized cross correlation is used for watermark detection. The original image is not required during the detection. Experiments are conducted to evaluate the robustness of the proposed method against different attacks on several images

Application of Digital Fingerprinting: Duplicate Image Detection

Identifying the content automatically is the most necessary condition to detect and fight piracy. Watermarking the image is the most basic and common technique to fight piracy. But the effectiveness of watermark is limited. Image fingerprinting provides an alternate and efficient solution for managing and identifying the multimedia content. After registering the original image contents, by comparing the colluded image with the original one, the percentage of distortion can be calculated. In this paper presented are one such fingerprinting-based forensic application: Duplicate image detection. To authenticate image content perceptual hash is an efficient solution. Perceptual hashes of almost similar images or near duplicate images are very similar to each other making it easier to compare images unlike cryptographic hashes which vary very radically even in the case of small distortions. Potential applications are unlimited including digital forensics, protection of copyrighted material etc. However, conventional image hash algorithms only offer a limited authentication level for the protection of overall content. In this work, we compared and contrasted different perceptual hashes and proposed a image hashing algorithm which is an excellent trade off of accuracy and speed

Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography

Data hiding is the process of embedding information into a noise-tolerant signal such as a piece of audio, video, or image. Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. Steganography, another form of data hiding, embeds data for the purpose of secure and secret communication. This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection methods. The objective functions, evaluation metrics, and datasets used for training these data hiding models are comprehensively summarised. Finally, we propose and discuss possible future directions for research into deep data hiding techniques