While the significant advancements have made in the generation of deepfakes using deep learning technologies, its misuse is a well-known issue now. Deepfakes can cause severe security and privacy issues as they can be used to impersonate a person's identity in a video by replacing his/her face with another person's face. Recently, a new problem of generating synthesized human voice of a person is emerging, where AI-based deep learning models can synthesize any person's voice requiring just a few seconds of audio. With the emerging threat of impersonation attacks using deepfake audios and videos, a new generation of deepfake detectors is needed to focus on both video and audio collectively. To develop a competent deepfake detector, a large amount of high-quality data is typically required to capture real-world (or practical) scenarios. Existing deepfake datasets either contain deepfake videos or audios, which are racially biased as well. As a result, it is critical to develop a high-quality video and audio deepfake dataset that can be used to detect both audio and video deepfakes simultaneously. To fill this gap, we propose a novel Audio-Video Deepfake dataset, FakeAVCeleb, which contains not only deepfake videos but also respective synthesized lip-synced fake audios. We generate this dataset using the most popular deepfake generation methods. We selected real YouTube videos of celebrities with four ethnic backgrounds to develop a more realistic multimodal dataset that addresses racial bias, and further help develop multimodal deepfake detectors. We performed several experiments using state-of-the-art detection methods to evaluate our deepfake dataset and demonstrate the challenges and usefulness of our multimodal Audio-Video deepfake dataset.
AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection
Social Networks and Human-Centered Computing Program, Taiwan International Graduate Program, Academia Sinica（社会网络与以人为本计算计划、台湾国际研究生计划、中国科学院）；Institute of Information Systems and Applications, National Tsing Hua University（信息系统与应用研究所、国立清华大学）；Department of Computer Science, National Chengchi University（计算机科学系、国立成功大学）；Research Center for Information Technology Innovation, Academia Sinica（信息技术创新研究中心、中国科学院）；Institute of Information Science, Academia Sinica（信息科学研究所、中国科学院）
Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
Audio DeepFakes are utterances generated with the use of deep neural networks. They are highly misleading and pose a threat due to use in fake news, impersonation, or extortion. In this work, we focus on increasing accessibility to the audio DeepFake detection methods by providing SpecRNet, a neural network architecture characterized by a quick inference time and low computational requirements. Our benchmark shows that SpecRNet, requiring up to about 40% less time to process an audio sample, provides performance comparable to LCNN architecture - one of the best audio DeepFake detection models.…
Audio DeepFakes allow the creation of high-quality, convincing utterances and therefore pose a threat due to its potential applications such as impersonation or fake news. Methods for detecting these manipulations should be characterized by good generalization and stability leading to robustness against attacks conducted with techniques that are not explicitly included in the training. In this work, we introduce Attack Agnostic Dataset - a combination of two audio DeepFakes and one anti-spoofing datasets that, thanks to the disjoint use of attacks, can lead to better generalization of detectio…
Anhui University（安徽大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；Qiyuan Laboratory（启元实验室）
Recently, pioneer research works have proposed a large number of acoustic features (log power spectrogram, linear frequency cepstral coefficients, constant Q cepstral coefficients, etc.) for audio deepfake detection, obtaining good performance, and showing that different subbands have different contributions to audio deepfake detection. However, this lacks an explanation of the specific information in the subband, and these features also lose information such as phase. Inspired by the mechanism of synthetic speech, the fundamental frequency (F0) information is used to improve the quality of sy…
University of Bucharest（布加勒斯特大学）；MBZ University of Artificial Intelligence（MBZ人工智能大学）；Linköping University（利德大学）；University of Central Florida（佛罗里达中央大学）
Audio-Visual Deepfake Detection With Local Temporal Inconsistencies
Interdisciplinary Centre for Security, Reliability and Trust (SnT)（安全、可靠性与跨学科研究中心（SnT））；University of Luxembourg（卢森堡大学）；National School of Computer Sciences (ENSI)（国立计算机科学学院（ENSI））；Manouba University（马努巴大学）
Significant advancements made in the generation of deepfakes have caused security and privacy issues. Attackers can easily impersonate a person's identity in an image by replacing his face with the target person's face. Moreover, a new domain of cloning human voices using deep-learning technologies is also emerging. Now, an attacker can generate realistic cloned voices of humans using only a few seconds of audio of the target person. With the emerging threat of potential harm deepfakes can cause, researchers have proposed deepfake detection methods. However, they only focus on detecting a sing…
DeepAgent: A Dual Stream Multi Agent Fusion for Robust Multimodal Deepfake Detection
Applied Artificial Intelligence and Intelligent Systems (AAIINS) Laboratory（应用人工智能与智能系统实验室）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Scholars（学者大学）；Faculty of Science and Information Technology（科学与信息技术学院）；Faculty of Science and Technology（科学与技术学院）
Investigating the Viability of Employing Multi-modal Large Language Models in the Context of Audio Deepfake Detection
Indian Institute of Technology, Ropar, India（印度理工学院罗帕尔分校）；Machine Intelligence Group, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, India（比拉理工科学院帕利尼 Hyderabad 分校机器智能小组）；Monash University, Melbourne, Australia（墨尔本大学）
Shandong University（山东大学）；National University of Singapore（新加坡国立大学）；The University of Hong Kong（香港大学）；University of Technology Sydney（悉尼科技大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data. However, the growing technique on these artifacts keeps challenging the robustness of traditional deepfake detectors. As a result, the development of generalizability of these approaches has reached a blockage. To address this issue, given the empirical results that the identities behind voices and faces are often mismatched in deepfake videos, and the voices and faces have homogeneity to some ex…
Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；Monash University（莫纳什大学）
We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to dis-harmony between the two modalities, eg, loss of lip-sync, unnatural facial and lip movements, etc. MDS is computed as an aggregate of dissimilarity scores between audio and visual segments in a video. Discriminative features are learnt for the audio and visual channels in a chunk-wise manner, employing the cross-entropy loss for individual modalities, and a contrastive loss…
We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake d…
Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）
Social Networks and Human-Centered Computing Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所台湾国际研究生计划社交网络与人本计算项目）；Department of Computer Science, National Chengchi University（国立政治大学资讯科学系）；Institute of Information Systems and Applications, National Tsing Hua University（国立清华大学资讯系统与应用研究所）；National Institute of Informatics（国立情报学研究所）；Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University（国立清华大学电机工程学系与通讯工程研究所）；Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）
Towards Generalizable Deepfake Detection via Forgery-aware Audio-Visual Adaptation: A Variational Bayesian Approach
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Department of New Networks, Pengcheng Laboratory（鹏城实验室网络部）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络安全科学与技术学院）；School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；School of Cyberspace Science, Harbin Institute of Technology（哈尔滨工业大学网络空间科学学院）
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies
USC Information Sciences Institute（南加州大学信息科学研究所）；USC Ming Hsieh Department of Electrical and Computer Engineering（南加州大学明氏电气与计算机工程学院）；Clemson University（克莱姆森大学）；Holcombe Department of Electrical and Computer Engineering（霍尔科姆电气与计算机工程学院）
LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization
School of Computer Science and Engineering（计算机科学与工程学院）；UNSW Sydney（新南威尔士大学悉尼分校）；School of Information and Communication Technology（信息与通信技术学院）；Griffith University（格里菲斯大学）
