As of late an AI based free programming device has made it simple to make authentic face swaps in recordings that leaves barely any hints of control, in what are known as "deepfake" recordings. Situations where these genuine istic counterfeit recordings are utilized to make political pain, extort somebody or phony fear based oppression occasions are effectively imagined. This paper proposes a transient mindful pipeline to automat-ically recognize deepfake recordings. Our framework utilizes a convolutional neural system (CNN) to remove outline level highlights. These highlights are then used to prepare a repetitive neural net-work (RNN) that figures out how to characterize if a video has been sub-ject to control or not. We assess our technique against a huge arrangement of deepfake recordings gathered from different video sites. We show how our framework can accomplish aggressive outcomes in this assignment while utilizing a basic design.
Deeplearning has been used to solve complex problems in various domains. As it advances, it also creates applications which become a major threat to our privacy, security and even to our Democracy. Such an application which is being developed recently is the "Deepfake". Deepfake models can create fake images and videos that humans cannot differentiate them from the genuine ones. Therefore, the counter application to automatically detect and analyze the digital visual media is necessary in today world. This paper details retraining the image classification models to apprehend the features from …
Deepfakes have become a critical social problem, and detecting them is of utmost importance. Also, deepfake generation methods are advancing, and it is becoming harder to detect. While many deepfake detection models can detect different types of deepfakes separately, they perform poorly on generalizing the detection performance over multiple types of deepfake. This motivates us to develop a generalized model to detect different types of deepfakes. Therefore, in this work, we introduce a practical digital forensic tool to detect different types of deepfakes simultaneously and propose Transfer l…
GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking
School of Computer Science and Technology, Xinjiang University（新疆大学计算机科学与技术学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）
Deepfake Forensics Adapter: A Dual-Stream Network for Generalizable Deepfake Detection
Shenzhen Technology University（深圳技术大学）；Singapore Institute of Technology（新加坡理工学院）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）
Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
College of Computer Science and Technology（计算机科学与技术学院）；Xinjiang University（新疆大学）；College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；Hunan University（湖南大学）
One of the most terrifying phenomenon nowadays is the DeepFake: the possibility to automatically replace a person's face in images and videos by exploiting algorithms based on deep learning. This paper will present a brief overview of technologies able to produce DeepFake images of faces. A forensics analysis of those images with standard methods will be presented: not surprisingly state of the art techniques are not completely able to detect the fakeness. To solve this, a preliminary idea on how to fight DeepFake images of faces will be presented by analysing anomalies in the frequency domain…
The Deepfake Detective: Interpreting Neural Forensics Through Sparse Features and Manifolds
Berkeley AI Safety Initiative (BASIS) University of California, Berkeley（伯克利人工智能安全倡议（BASIS）大学伯克利）；Department of Electrical and Computer Engineering Johns Hopkins University（电气与计算机工程系约翰霍普金斯大学）
Deepfake Media Forensics: State of the Art and Challenges Ahead
University of Catania（卡塔尼亚大学）；CNIT（意大利国家电信集团）；Universitas Mercatorum（商业大学）；University of Pisa（比萨大学）；University of Cagliari（卡利亚里大学）；Sapienza University of Rome（罗马第一大学）；Politecnico di Milano（米兰理工大学）；University of Messina（墨西拿大学）；Università di Siena（锡耶纳大学）；University of Trento（特伦托大学）；Scuola IMT Alti Studi Lucca（卢卡高等研究学院）
Deepfake face not only violates the privacy of personal identity, but also confuses the public and causes huge social harm. The current deepfake detection only stays at the level of distinguishing true and false, and cannot trace the original genuine face corresponding to the fake face, that is, it does not have the ability to trace the source of evidence. The deepfake countermeasure technology for judicial forensics urgently calls for deepfake traceability. This paper pioneers an interesting question about face deepfake, active forensics that "know it and how it happened". Given that deepfake…
University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；University of Chinese Academy of Sciences（中国科学院大学）
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFa…
University of Catania（卡塔尼亚大学）；iCTLab Spinoff of University of Catania（卡塔尼亚大学iCTLab衍生公司）；Banca d’Italia（意大利银行）
Most recent style-transfer techniques based on generative architectures are able to obtain synthetic multimedia contents, or commonly called deepfakes, with almost no artifacts. Researchers already demonstrated that synthetic images contain patterns that can determine not only if it is a deepfake but also the generative architecture employed to create the image data itself. These traces can be exploited to study problems that have never been addressed in the context of deepfakes. To this aim, in this paper a first approach to investigate the image ballistics on deepfake images subject to style…
LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection
The Hong Kong Polytechnic University（香港理工大学）；University College London（伦敦大学学院）；Tsinghua University（清华大学）；Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）
All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark
School of Computer Science and Technology, Xinjiang University, Urumqi, China（新疆大学计算机科学与技术学院）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center, Urumqi, China（新疆多模态智能处理与信息安全工程技术研究中心）
University of Macau（澳门大学）；Macau University of Science and Technology（澳门科技大学）
With the continuous research on Deepfake forensics, recent studies have attempted to provide the fine-grained localization of forgeries, in addition to the coarse classification at the video-level. However, the detection and localization performance of existing Deepfake forensic methods still have plenty of room for further improvement. In this work, we propose a Spatial-Temporal Deepfake Detection and Localization (ST-DDL) network that simultaneously explores spatial and temporal features for detecting and localizing forged regions. Specifically, we design a new Anchor-Mesh Motion (AMM) algor…
IRIS-GAN: Staged Specialist Detection of Deepfake Faces
Departamento de Física Teórica, Universitat de València, Burjassot, Spain（瓦伦西亚大学理论物理系，瓦伦西亚大学，西班牙Burjassot）；Instituto de Física Corpuscular (IFIC), CSIC–Universitat de València, Valencia, Spain（物理微观粒子研究所（IFIC），西班牙-瓦伦西亚大学，瓦伦西亚，西班牙）
ByteDance AI Lab（字节跳动人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）
With the progress in AI-based facial forgery (i.e., deepfake), people are increasingly concerned about its abuse. Albeit effort has been made for training classification (also known as deepfake detection) models to recognize such forgeries, existing models suffer from poor generalization to unseen forgery technologies and high sensitivity to changes in image/video quality. In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. We believe training with samples that are adversarially crafted to at…
Hanoi University of Science and Technology（河内理工大学）；Griffith University（格里菲斯大学）；Leibniz Universität Hannover（汉诺威莱布尼茨大学）；Deakin University（迪肯大学）；The University of Queensland（昆士兰大学）
In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. In this paper, we present a benchmark that provides in-depth insights into visual forgery and visual forens…
The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on ad-hoc Generative Adversarial Networks (GAN). In this work we focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method able to detect a forensics trace hidden in images: a sort of fingerprint left in the image generation process. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying…
Google Brain（谷歌大脑）；University of California, Berkeley（加州大学伯克利分校）
It is now possible to synthesize highly realistic images of people who don't exist. Such content has, for example, been implicated in the creation of fraudulent social-media profiles responsible for dis-information campaigns. Significant efforts are, therefore, being deployed to detect synthetically-generated content. One popular forensic approach trains a neural network to distinguish real from synthetic content. We show that such forensic classifiers are vulnerable to a range of attacks that reduce the classifier to near-0% accuracy. We develop five attack case studies on a state-of-the-art …
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光耀医学院）；Yunnan United Vision Technology Co., Ltd（云南联合视觉科技有限公司）；Department of Computer Science and Engineering, University at Buffalo, State University of New York（纽约州立大学布法罗分校计算机科学与工程系）
Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc. The results are utterly realistic and extremely easy to be exploited even for non-experienced users. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. Indeed, discriminating a Deepfake f…
The current spike of hyper-realistic faces artificially generated using deepfakes calls for media forensics solutions that are tailored to video streams and work reliably with a low false alarm rate at the video level. We present a method for deepfake detection based on a two-branch network structure that isolates digitally manipulated faces by learning to amplify artifacts while suppressing the high-level face content. Unlike current methods that extract spatial frequencies as a preprocessing step, we propose a two-branch structure: one branch propagates the original information, while the ot…
Universität Bonn（波恩大学）；Fraunhofer SCAI（弗劳恩霍夫算法与科学计算研究所）；University of Bonn（波恩大学）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Fraunhofer ITWM（弗劳恩霍夫工业数学研究所）；Institute for Numerical Simulation（数值模拟研究所）
As neural networks become able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable method toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space CNNs or the Fourier transform. To the best of our knowledge, synthesized fake image analysis and detection methods based on a multi-scale wavelet representation, l…
