Online Deception in Social Media

The unknown and the invisible exploit the unwary and the uninformed for illicit financial gain and reputation damage

A new technique for video copy-move forgery detection

This thesis describes an algorithm for detecting copy-move falsifications in digital video. The thesis is composed of 5 chapters. In the first chapter there is an introduction to forgery detection for digital images and videos. Chapters 2, 3 and 4 describe in detail the techniques used for the implementation of the detection algorithm. The experimental results are presented in the fifth and last chapter

How Generalizable are Deepfake Detectors? An Empirical Study

Deepfake videos and images are becoming increasingly credible, posing a significant threat given their potential to facilitate fraud or bypass access control systems. This has motivated the development of deepfake detection methods, in which deep learning models are trained to distinguish between real and synthesized footage. Unfortunately, existing detection models struggle to generalize to deepfakes from datasets they were not trained on, but little work has been done to examine why or how this limitation can be addressed. In this paper, we present the first empirical study on the generalizability of deepfake detectors, an essential goal for detectors to stay one step ahead of attackers. Our study utilizes six deepfake datasets, five deepfake detection methods, and two model augmentation approaches, confirming that detectors do not generalize in zero-shot settings. Additionally, we find that detectors are learning unwanted properties specific to synthesis methods and struggling to extract discriminative features, limiting their ability to generalize. Finally, we find that there are neurons universally contributing to detection across seen and unseen datasets, illuminating a possible path forward to zero-shot generalizability.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Video inpainting for non-repetitive motion

Master'sMASTER OF SCIENC

DEEPFAKE IN ONLINE FRAUD CASES: THE HAZE OF ARTIFICIAL INTELLIGENCE’S ACCOUNTABILITY BASED ON THE INTERNATIONAL LAW

Artificial Intelligence (AI) is the science and engineering of intelligent machines, primarily through computer programs. AI consists of processes by human intelligence simulated through machine processes and is concerned with designing, developing, and implementing computer systems. One of them is Deepfake. Deepfake is a technology that uses data in the form of an image/photo of a person's face, which is part of personal data and potentially misused to commit crimes such as online loan fraud. The research discusses (1) the concept of accountability for deepfake artificial intelligence in online loan fraud according to international law and its application in Indonesia and (2) the analysis of regulations and accountability of deep fake according to chaos theory. The research uses legal-normative approach. Moreover, the research will examine legal principles, systematics, and comparative law in its application. The research illustrates that the concept of deepfake accountability as artificial intelligence in online loan fraud according to international law is described in the General Recommendation on the Ethics of Artificial Intelligence by UNESCO. Meanwhile, the concept of accountability in Indonesia is seen in Human Rights and Data Privacy Violations. According to chaos theory, the analysis of deepfake’s regulatory and accountability concept  in international and national law leads to inconsistencies because it is only a recommendation, yet to be integrated, and still multidimensional