Lightweight MobileNet Model for Image Tempering Detection

In recent years, there has been a wide range of image manipulation identification challenges and an overview of image tampering detection and the relevance of applying deep learning models such as CNN and MobileNet for this purpose. The discussion then delves into the construction and setup of these models, which includes a block diagram as well as mathematical calculations for each layer. A literature study on Image tampering detection is also included in the discussion, comparing and contrasting various articles and their methodologies. The study then moves on to training and assessment datasets, such as the CASIA v2 dataset, and performance indicators like as accuracy and loss. Lastly, the performance characteristics of the MobileNet and CNN designs are compared. This work focuses on Image tampering detection using convolutional neural networks (CNNs) and the MobileNet architecture. We reviewed the MobileNet architecture's setup and block diagram, as well as its application to Image tampering detection. We also looked at significant literature on Image manipulation detection, such as major studies and their methodologies. Using the CASIA v2 dataset, we evaluated the performance of MobileNet and CNN architectures in terms of accuracy and loss. This paper offered an overview of the usage of deep learning and CNN architectures for image tampering detection and proved their accuracy in detecting manipulated images

Organizing data analytics

We develop a theory of credible skepticism in organizations to explain the main tradeoffs in organizing data generation, analysis, and reporting. In our designer-agent-principal game, the designer selects the information privately observed by the agent who can misreport it at a cost, whereas the principal can audit the report. We study three organizational levers: tampering prevention, tampering detection, and the allocation of the experimental-design task. We show that motivating informative experimentation while discouraging misreporting are often conflicting organizational goals. To incentivize experimentation, the principal foregoes a flawless tampering detection/prevention system and separates the tasks of experimental design and analysis

The PS-Battles Dataset - an Image Collection for Image Manipulation Detection

The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or tampered with. As derivations can be done with malicious intent, there is an urgent need for reliable and easily usable tampering detection methods. However, even media considered semantically untampered by humans might have already undergone compression steps or light post-processing, making automated detection of tampering susceptible to false positives. In this paper, we present the PS-Battles dataset which is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.Comment: The dataset introduced in this paper can be found on https://github.com/dbisUnibas/PS-Battle

A review of digital video tampering: from simple editing to full synthesis.

Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with discussions of future research directions aimed at improving detection methods

ARCHANGEL: Tamper-proofing Video Archives using Temporal Content Hashes on the Blockchain

We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we describe a novel deep network architecture for computing compact temporal content hashes (TCHs) from audio-visual streams with durations of minutes or hours. Our TCHs are sensitive to accidental or malicious content modification (tampering) but invariant to the codec used to encode the video. This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, Estonia and Norway participated.Comment: Accepted to CVPR Blockchain Workshop 201

Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future