With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now guarantee a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. So-called deepfakes can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Potential abuses are limited only by human imagination. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes and, from the point of view of the forensic analyst, on modern data-driven forensic methods. The analysis will help to highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.

Media Forensics and DeepFakes: an overview

This paper presents a chaotic encryption-based blind digital image watermarking technique applicable to both grayscale and color images. Discrete cosine transform (DCT) is used before embedding the watermark in the host image. The host image is divided into $8\times 8$ nonoverlapping blocks prior to DCT application, and the watermark bit is embedded by modifying difference between DCT coefficients of adjacent blocks. Arnold transform is used in addition to chaotic encryption to add double-layer security to the watermark. Three different variants of the proposed algorithm have been tested and analyzed. The simulation results show that the proposed scheme is robust to most of the image processing operations like joint picture expert group compression, sharpening, cropping, and median filtering. To validate the efficiency of the proposed technique, the simulation results are compared with certain state-of-art techniques. The comparison results illustrate that the proposed scheme performs better in terms of robustness, security, and imperceptivity. Given the merits of the proposed scheme, it can be used in applications like e-healthcare and telemedicine to robustly hide electronic health records in medical images.

Secure and Robust Digital Image Watermarking Using Coefficient Differencing and Chaotic Encryption

Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, applications of Convolutional Neural Networks for watermarking have recently emerged. In this paper, we propose a deep end-to-end diffusion watermarking framework (ReDMark) which can learn a new watermarking algorithm in any desired transform space. The framework is composed of two Fully Convolutional Neural Networks with residual structure which handle embedding and extraction operations in real-time. The whole deep network is trained end-to-end to conduct a blind secure watermarking. The proposed framework simulates various attacks as a differentiable network layer to facilitate end-to-end training. The watermark data is diffused in a relatively wide area of the image to enhance security and robustness of the algorithm. Comparative results versus recent state-of-the-art researches highlight the superiority of the proposed framework in terms of imperceptibility, robustness and speed. The source codes of the proposed framework are publicly available at Github 1 .

ReDMark: Framework for Residual Diffusion Watermarking based on Deep Networks

Finding robust domain from attacks: A learning framework for blind watermarking

A robust blind color image watermarking based on Fourier transform domain

Detecting and localizing image splicing has become essential to fight against malicious forgery. A major challenge to localize spliced areas is to discriminate between authentic and tampered regions with intrinsic properties such as compression artifacts. We propose CAT-Net, an end-to-end fully convolutional neural network including RGB and DCT streams, to learn forensic features of compression artifacts on RGB and DCT domains jointly. Each stream considers multiple resolutions to deal with spliced object’s various shapes and sizes. The DCT stream is pretrained on double JPEG detection to utilize JPEG artifacts. The proposed method outperforms state-of-the-art neural networks for localizing spliced regions in JPEG or non-JPEG images.

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CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing

Depth image based rendering (DIBR) is a promising technique for extending viewpoints with a monoscopic center image and its associated per-pixel depth map. With its numerous advantages including low-cost bandwidth, 2D-to-3D compatibility and adjustment of depth condition, DIBR has received much attention in the 3D research community. In the case of a DIBR-based broadcasting system, a malicious adversary can illegally distribute both a center view and synthesized virtual views as 2D and 3D content, respectively. To deal with the issue of copyright protection for DIBR 3D Images, we propose a scale invariant feature transform (SIFT) features based blind watermarking algorithm. To design the proposed method robust against synchronization attacks from DIBR operation, we exploited the parameters of the SIFT features: the location, scale and orientation. Because the DIBR operation is a type of translation transform, the proposed method uses high similarity between the SIFT parameters extracted from a synthesized virtual view and center view images. To enhance the capacity and security, we propose an orientation of keypoints based watermark pattern selection method. In addition, we use the spread spectrum technique for watermark embedding and perceptual masking taking into consideration the imperceptibility. Finally, the effectiveness of the presented method was experimentally verified by comparing with other previous schemes. The experimental results show that the proposed method is robust against synchronization attacks from DIBR operation. Furthermore, the proposed method is robust against signal distortions and typical attacks from geometric distortions such as translation and cropping.

A SIFT features based blind watermarking for DIBR 3D images

Double compression (DC) involves compressing a video twice with the video codec. DC usually occurs when a compressed video is manipulated, because most videos are saved in the compression format after manipulating. Given this principle, if we can detect DC traces in videos, we can determine whether a video has been manipulated. Prior works have proposed methods for detecting DC of H.264 videos; however, they have limitations in that they can detect DC only for a limited dataset and set of compression parameters. To overcome these limitations, we propose a two-stream neural network that incorporates two components that analyze intra-coded frames and predictive frames. We explain why the proposed two-stream neural network can detect DC traces in videos more accurately than the prior method, and we demonstrate that the proposed network can detect DC videos that have various contents and parameters through extensive experiments.

Two-Stream Network for Detecting Double Compression of H.264 Videos

Content-aware image resizing is the process of adjusting the size of an image while preserving its important content. Image resizing is used to overcome diversity in resolutions between modules, such as display devices and applications, and can thus be deliberately exploited to distort or remove original content; therefore, detecting such tampering has become an important topic in forensics. This paper proposes a deep neural network architecture to capture subtle local artifacts caused by seam-based image resizing. Unlike past approaches that only classified two classes, our approach is the first attempt to solve a given forensic task with three-class classification: original, seam insertion, and seam carving. The experimental results show that our work performs better than the handcrafted feature-based method and networks designed for different forensic tasks.

Seung-Hun Nam

Papers

Finding robust domain from attacks: A learning framework for blind watermarking

CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing

A SIFT features based blind watermarking for DIBR 3D images

Two-Stream Network for Detecting Double Compression of H.264 Videos

Content-Aware Image Resizing Detection Using Deep Neural Network