Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and the previous studies to originate from the frequency-level artifacts in generated images. We find that ignoring the frequency-level artifacts can improve the detector's generalization across various GAN models, but it can reduce the model's performance for the trained GAN models. Thus, we design a framework to generalize the deepfake detector for both the known and unseen GAN models. Our framework generates the frequency-level perturbation maps to make the generated images indistinguishable from the real images. By updating the deepfake detector along with the training of the perturbation generator, our model is trained to detect the frequency-level artifacts at the initial iterations and consider the image-level irregularities at the last iterations. For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories. Numerous experiments validate the state-of-the-art performance of our deepfake detector.
This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors. We created adversarial perturbations using the Fast Gradient Sign Method and the Carlini and Wagner L2 norm attack in both blackbox and whitebox settings. Detectors achieved over 95% accuracy on unperturbed deepfakes, but less than 27% accuracy on perturbed deepfakes. We also explore two improvements to deepfake detectors: (i) Lipschitz regularization, and (ii) Deep Image Prior (DIP). Lipschitz regularization constrains the gradient of the detector with respect to the input in order to incre…
CAE-Net: Generalized Deepfake Image Detection using Convolution and Attention Mechanisms with Spatial and Frequency Domain Features
Department of Electrical and Electronic Engineering, BUET, Dhaka 1205, Bangladesh（电子与电气工程系，布里克塔大学（BUET），达卡1205，孟加拉国）；Department of Computer Science and Engineering BRAC University Dhaka 1212, Bangladesh（计算机科学与工程系，布拉克大学（BRAC University），达卡1212，孟加拉国）
Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection
State Key Laboratory of Blockchain and Data Security, Zhejiang University（区块链与数据安全国家重点实验室，浙江大学）；College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；State Grid Blockchain Technology (Beijing) Co., Ltd.（国家电网区块链技术（北京）有限公司）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）；Hangzhou City University（杭州城市学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
Towards Sustainable Universal Deepfake Detection with Frequency-Domain Masking
Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）；Faculty of Science and Technology (REALTEK), Norwegian University of Life Sciences (NMBU)（科学与技术学院（REALTEK）、挪威生命科学大学）
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；School of Information Science and Engineering, Yanshan University（燕山大学信息科学与工程学院）；Center for Frontier AI Research, IHPC, A*STAR, Singapore（新加坡科技研究局前沿人工智能研究中心）
Facially manipulated images and videos or DeepFakes can be used maliciously to fuel misinformation or defame individuals. Therefore, detecting DeepFakes is crucial to increase the credibility of social media platforms and other media sharing web sites. State-of-the art DeepFake detection techniques rely on neural network based classification models which are known to be vulnerable to adversarial examples. In this work, we study the vulnerabilities of state-of-the-art DeepFake detection methods from a practical stand point. We perform adversarial attacks on DeepFake detectors in a black box set…
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）数据科学学院）
A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））；Shandong Artificial Intelligence Institute, China（山东省人工智能研究院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Key Laboratory of Computing Power Network（计算能力网络关键实验室）
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）
Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on deep neural networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We furt…
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Deep convolutional neural networks have achieved exceptional results on multiple detection and recognition tasks. However, the performance of such detectors are often evaluated in public benchmarks under constrained and non-realistic situations. The impact of conventional distortions and processing operations found in imaging workflows such as compression, noise, and enhancement are not sufficiently studied. Currently, only a few researches have been done to improve the detector robustness to unseen perturbations. This paper proposes a more effective data augmentation scheme based on real-worl…
IIT Jodhpur（印度理工学院焦特布尔分校）；University at Buffalo（布法罗大学）；IIT Kharagpur（印度理工学院卡拉格普尔分校）
The rapid progress in the ease of creating and spreading ultra-realistic media over social platforms calls for an urgent need to develop a generalizable deepfake detection technique. It has been observed that current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. Inspired by this observation, in this paper, we present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation for classifying \textit{deepfakes}. MD-CSDNetwork is a novel …
Enhanced Deep Learning DeepFake Detection Integrating Handcrafted Features
Facephi Biometrics S.A.（Facephi 生物特征技术公司）；da/sec-Biometrics and Internet Security Research Group（生物特征与网络安全研究小组）；Internet Security Research Group（网络安全研究小组）
Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the…
Curvelet-Based Frequency-Aware Feature Enhancement for Deepfake Detection
Department of Computer Science, College of Science, University of Zakho（扎赫大学计算机科学系）；PRIME Lab, Scientific Research Center, University of Zakho（扎赫大学PRIME实验室）
Binghamton University（宾汉姆顿大学）；The U.S. Air Force Research Laboratory（美国空军研究实验室）
Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluct…
Evading DeepFake Detectors via Adversarial Statistical Consistency
Kyushu University（九州大学）；Centre for Frontier AI Research (CFAR), A*STAR（新加坡科技研究局前沿人工智能研究中心）；Institute of High Performance Computing (IHPC), A*STAR（新加坡科技研究局高性能计算研究所）；Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；University of Alberta（阿尔伯塔大学）；The University of Tokyo（东京大学）
Indian Institute of Technology Ropar（印度理工学院罗巴尔分校）；Monash University（莫纳什大学）
This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media. FakeBuster is a standalone deep learning based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conferencing based meeting. This tool is independent of video conferencing solutions and has been tested with Zoom and Skype applications. It uses a 3D convolutional neural network for predicting video segment-wise fakeness scores. The network is trained on a combination of datasets such as Deeperfor…
Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning
Research Center for Information Technology Innovation, Academia Sinica（中央研究院資訊科技創新研究中心）；National Chengchi University（國立政治大學）；National Taiwan University（國立臺灣大學）
ARMOR++: Agentic Orchestration of a Multi-Domain Primitive Set for Transferable Attacks on Deepfake Detectors
Department of Informatics, Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学信息学系）；Infocomm Technology Cluster, Singapore Institute of Technology（新加坡科技学院信息通信技术集群）；Department of Applied Physics and Applied Mathematics, Columbia University（哥伦比亚大学应用物理与应用数学系）；Division of Natural and Applied Sciences, Duke Kunshan University（昆山杜克大学自然科学与应用科学部）；Department of Electrical and Computer Engineering, University of Toronto（多伦多大学电气与计算机工程系）
The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models. In particular, we introduce a Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels. M2TR further learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block. In addition…
A Multi-Domain Feature Fusion Framework for Generalizable Deepfake Detection Across Different Generators
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan（巴基斯坦国立科技大学电气工程与计算机科学学院）；Department of Communication, Quality Management and Information Systems, Mid Sweden University, Ostersund Campus, Sweden（瑞典中部大学通信、质量管理和信息系统系，厄斯特松德校区）；Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Luleå, Sweden（瑞典吕勒奥理工大学计算机科学、电气与空间工程系）
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images. Thus, it is important to develop a generalized detector for synthesized images of any GAN model or object category, including those unseen during the training phase. However, the conventional methods heavily depend on the training settings, which cause a dramatic decline in performance when tested with unknown domains. To resolve the issue and obtain a generalized detection ability, we propose Bilateral…
Wavelet-Driven Generalizable Framework for Deepfake Face Forgery Detection
International Institute of Information Technology Hyderabad（印度信息技术学院海得拉巴分校）；Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）；Michigan State University（密歇根州立大学）
WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks
College of Computer Science and Technology, Xinjiang University（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（新疆多模态智能处理与信息安全工程技术研究中心）；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）；School of Computer Science, Hefei University of Technology（合肥工业大学计算机科学与技术学院）
University of Toronto（多伦多大学）；Vector Institute（矢量研究院）；University of Cambridge（剑桥大学）
Progress in generative modelling, especially generative adversarial networks, have made it possible to efficiently synthesize and alter media at scale. Malicious individuals now rely on these machine-generated media, or deepfakes, to manipulate social discourse. In order to ensure media authenticity, existing research is focused on deepfake detection. Yet, the adversarial nature of frameworks used for generative modeling suggests that progress towards detecting deepfakes will enable more realistic deepfake generation. Therefore, it comes at no surprise that developers of generative models are …
DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not requir…
University of Maryland（马里兰大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs). Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. While existing research work on deepfake detection demonstrates high accuracy, it is subject to advances in generation techniques and adversarial iterations on detection countermeasure techniques. Thus, we seek a proactive and sustainable solution on deepfake detection, that is agnostic to the evolution of generative models, by introducing ar…
RoGA: Towards Generalizable Deepfake Detection through Robust Gradient Alignment
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（计算机科学与技术学院，南京航空航天大学）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息科技部模式分析与机器智能重点实验室）
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…
