Detecting maliciously falsified facial images and videos has attracted extensive attention from digital-forensics and computer-vision communities. An important topic in manipulation detection is the localization of the fake regions. Previous work related to forgery detection mostly focuses on the entire faces. However, recent forgery methods have developed to edit important facial components while maintaining others unchanged. This drives us to not only focus on the forgery detection but also fine-grained falsified region segmentation. In this paper, we propose a collaborative feature learning approach to simultaneously detect manipulation and segment the falsified components. With the collaborative manner, detection and segmentation can boost each other efficiently. To enable our study of forgery detection and segmentation, we build a facial forgery dataset consisting of both entire and partial face forgeries with their pixel-level manipulation ground-truth. Experiment results have justified the mutual promotion between forgery detection and manipulated region segmentation. The overall performance of the proposed approach is better than the state-of-the-art detection or segmentation approaches. The visualization results have shown that our proposed model always captures the artifacts on facial regions, which is more reasonable.
University of Sydney（悉尼大学）；UNSW Canberra（新南威尔士大学堪培拉分校）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61研究所）
Facial forgery detection is a crucial but extremely challenging topic, with the fast development of forgery techniques making the synthetic artefact highly indistinguishable. Prior works show that by mining both spatial and frequency information the forgery detection performance of deep learning models can be vastly improved. However, leveraging multiple types of information usually requires more than one branch in the neural network, which makes the model heavy and cumbersome. Knowledge distillation, as an important technique for efficient modelling, could be a possible remedy. We find that e…
TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection
MAIS \& NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China University of Science
LAKAN: Landmark-assisted Adaptive Kolmogorov-Arnold Network for Face Forgery Detection
School of Cyber Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）
We propose Deep Distribution Transfer(DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery detection. We examine how well a model (pre-)trained with one forgery creation method generalizes towards a previously unseen manipulation technique or different dataset. To facilitate this transfer, we introduce a new mixture model-based loss formulation that learns a multi-modal distribution, with modes corresponding to class categories of the underlying data of the source forgery method. Our core idea is to first pre-train an enco…
MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios
Anhui Province Key Laboratory of Digital Security（安徽省级数字安全重点实验室）；Hefei University of Technology（合肥工业大学）；Cardiff University（卡迪夫大学）；IHPC, Agency for Science, Technology and Research（科技研究局信息处理中心）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline
Xi’an Jiaotong University（西安交通大学）；Hefei University of Technology（合肥工业大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；Northwestern Polytechnical University（西北工业大学）；University of Macau（澳门大学）
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…
DevFD: Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部门）；China Mobile Financial Technology Co., Ltd.（中国移动金融科技有限公司）；CAIR, HKSIS, Chinese Academy of Sciences（中国科学院CAIR部门，香港创新技术研究所）；School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T（澳门科技大学计算机科学与工程学院，创新工程学院）
School of Computer Science, Wuhan University（武汉大学计算机学院）
Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics. As forgery images and videos are usually compressed into different formats such as JPEG and H264 when circulating on the Internet, existing forgery-detection methods trained on uncompressed data often suffer from significant performance degradation in identifying them. To solve this problem, we propose a novel anti-compression facial forgery detection framework, which learns a compression-insensitive embedding feature space utilizing both original and compressed forgeries. Specifically, our a…
Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture. We address this issue by compressing facial makeup transfer networks with collaborative distillation and kernel decomposition. The main idea of collaborative distillation is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for low-level vision tasks. For kernel…
Presence of noise in the labels of large scale facial expression datasets has been a key challenge towards Facial Expression Recognition (FER) in the wild. During early learning stage, deep networks fit on clean data. Then, eventually, they start overfitting on noisy labels due to their memorization ability, which limits FER performance. This work proposes an effective training strategy in the presence of noisy labels, called as Consensual Collaborative Training (CCT) framework. CCT co-trains three networks jointly using a convex combination of supervision loss and consistency loss, without ma…
University of Science and Technology of China（中国科学技术大学）
Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features. The ideal discriminative features should be only related to the real/fake labels of facial images. However, we observe that the features learned by vanilla classification networks are correlated to unnecessary properties, such as forgery methods and facial identities. Such phenomenon would limit forgery detection performance especially for the generalization ability. Motivated by this, we propose a novel met…
University of Maryland College Park（马里兰大学帕克分校）；Johns Hopkins University（约翰斯·霍普金斯大学）
Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and gi…
University of Sydney（悉尼大学）；UNSW Canberra（新南威尔士大学堪培拉分校）；CSIRO Data61（澳大利亚联邦科学与工业研究组织数据61研究所）
Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We bel…
With the rapid development of generation model, AI-based face manipulation technology, which called DeepFakes, has become more and more realistic. This means of face forgery can attack any target, which poses a new threat to personal privacy and property security. Moreover, the misuse of synthetic video shows potential dangers in many areas, such as identity harassment, pornography and news rumors. Inspired by the fact that the spatial coherence and temporal consistency of physiological signal are destroyed in the generated content, we attempt to find inconsistent patterns that can distinguish…
SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；Anhui Province Key Laboratory of Digital Security（安徽省数字安全重点实验室）
FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning
State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）
Jawaharlal Nehru University（贾瓦哈拉尔·尼赫鲁大学）；Indian Institute of Technology Delhi（印度理工学院德里分校）
Predicting attributes in the landmark free facial images is itself a challenging task which gets further complicated when the face gets occluded due to the usage of masks. Smart access control gates which utilize identity verification or the secure login to personal electronic gadgets may utilize face as a biometric trait. Particularly, the Covid-19 pandemic increasingly validates the essentiality of hygienic and contactless identity verification. In such cases, the usage of masks become more inevitable and performing attribute prediction helps in segregating the target vulnerable groups from …
One of the most pressing challenges for the detection of face-manipulated videos is generalising to forgery methods not seen during training while remaining effective under common corruptions such as compression. In this paper, we examine whether we can tackle this issue by harnessing videos of real talking faces, which contain rich information on natural facial appearance and behaviour and are readily available in large quantities online. Our method, termed RealForensics, consists of two stages. First, we exploit the natural correspondence between the visual and auditory modalities in real vi…
Technical University of Munich（慕尼黑工业大学）；University Federico II of Naples（那不勒斯费德里科二世大学）；University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the be…
A Dual-Branch CNN for Robust Detection of AI-Generated Facial Forgeries
Computer Science Department University of Southern Maine Portland, United States（计算机科学系 俄亥俄州立大学 帕斯托市 美国）；The Department of Computer Science University of Central Oklahoma Edmond, United States（计算机科学系 中央俄克拉荷马大学 埃德蒙德 美国）
École des Ponts Paristech（巴黎高科桥梁学院）；JFLI, CNRS, UMI 3527（法国国家科学研究中心JFLI联合实验室）；LIGM, UMR 8049（计算机科学、建模与应用实验室）；UPEM（巴黎东马恩-拉瓦雷大学）；National Institute of Informatics（国立情报学研究所）
This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constitut…
EvolveReason: Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Ant Group（蚂蚁集团）
StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Beihang University（北京航空航天大学）；Macau University of Science and Technology（澳门科技大学）；Nanyang Technological University（南洋理工大学）
This paper presents an integration of image forgery detection with image facial recognition using black propagation neural network (BPNN). We observed that facial image recognition by itself will always give a matching output or closest possible output image for every input image irrespective of the authenticity or otherwise not of the testing input image. Based on this, we are proposing the combination of the blind but powerful automation image forgery detection for entire input images for the BPNN recognition program. Hence, an input image must first be authenticated before being fed into th…
Beijing University of Posts and Telecommunications（北京邮电大学）
Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery. Therefore, we propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Specifically, our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery. Especially,…
The University of Tokyo（东京大学）；National Institute of Informatics（国立信息学研究所）；Sun Yat-sen University（中山大学）；South China University of Technology（华南理工大学）
Deep-learning-based technologies such as deepfakes ones have been attracting widespread attention in both society and academia, particularly ones used to synthesize forged face images. These automatic and professional-skill-free face manipulation technologies can be used to replace the face in an original image or video with any target object while maintaining the expression and demeanor. Since human faces are closely related to identity characteristics, maliciously disseminated identity manipulated videos could trigger a crisis of public trust in the media and could even have serious politica…
Learning Real Facial Concepts for Independent Deepfake Detection
School of Software, Shandong University（山东大学软件学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Nanyang Technological University（南洋理工大学）
With the rapid development of facial manipulation techniques, face forgery detection has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. However, without considering the correlation between local regions, these global supervisions are insufficient to learn a generalized feature and prone to overfitting. To address this issue, we propose a novel perspective of face forgery detection via local relation learni…
SenseTime Research（商汤科技研究院）；University of Electronic Science and Technology of China（电子科技大学）；College of Software, Beihang University（北京航空航天大学软件学院）；Northwestern Polytechnical University（西北工业大学）
As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection. However, it is extremely challenging since recent advances are able to forge faces beyond the perception ability of human eyes, especially in compressed images and videos. We find that mining forgery patterns with the awareness of frequency could be a cure, as frequency provides a complementary viewpoint where either subtle forgery artifacts or compression errors could be well descr…
With the rapid development of facial forgery techniques, forgery detection has attracted more and more attention due to security concerns. Existing approaches attempt to use frequency information to mine subtle artifacts under high-quality forged faces. However, the exploitation of frequency information is coarse-grained, and more importantly, their vanilla learning process struggles to extract fine-grained forgery traces. To address this issue, we propose a progressive enhancement learning framework to exploit both the RGB and fine-grained frequency clues. Specifically, we perform a fine-grai…
With various facial manipulation techniques arising, face forgery detection has drawn growing attention due to security concerns. Previous works always formulate face forgery detection as a classification problem based on cross-entropy loss, which emphasizes category-level differences rather than the essential discrepancies between real and fake faces, limiting model generalization in unseen domains. To address this issue, we propose a novel face forgery detection framework, named Dual Contrastive Learning (DCL), which specially constructs positive and negative paired data and performs designe…
