Channel is one of the important criterions for digital audio quality. General-ly, stereo audio two channels can provide better perceptual quality than mono audio. To seek illegal commercial benefit, one might convert mono audio to stereo one with fake quality. Identifying of stereo faking audio is still a less-investigated audio forensic issue. In this paper, a stereo faking corpus is first present, which is created by Haas Effect technique. Then the effect of stereo faking on Mel Frequency Cepstral Coefficients (MFCC) is analyzed to find the difference between the real and faked stereo audio. Fi-nally, an effective algorithm for identifying stereo faking audio is proposed, in which 80-dimensional MFCC features and Support Vector Machine (SVM) classifier are adopted. The experimental results on three datasets with five different cut-off frequencies show that the proposed algorithm can ef-fectively detect stereo faking audio and achieve a good robustness.
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）
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）
EmoFake: An Initial Dataset for Emotion Fake Audio Detection
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；Department of Automation, Tsinghua University（清华大学自动化系）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；University of Science and Technology of China（中国科学技术大学）
Zero-Shot Fake Video Detection by Audio-Visual Consistency
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Center for Speech and Language Technologies, BNRist, Tsinghua University（清华大学BNR实验室语音与语言技术中心）
Half-Truth: A Partially Fake Audio Detection Dataset
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
本文构建 HAD 部分伪造音频检测数据集，采用最新语音合成技术仅替换语句中的少数词语，支持伪造检测与篡改区域定位，并通过基准实验证明其比完全伪造音频更具挑战性。
An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）
Detection of Cross-Dataset Fake Audio Based on Prosodic and Pronunciation Features
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
TO-Rawnet: Improving RawNet with TCN and Orthogonal Regularization for Fake Audio Detection
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；Anhui University（安徽大学）
A Noval Feature via Color Quantisation for Fake Audio Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；Department of Automation, Tsinghua University（清华大学自动化系）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）
Johns Hopkins University（约翰霍普金斯大学）；Brno University of Technology（布拉格技术大学）；National Institute of Informatics（国家信息研究所）；National University of Singapore（新加坡国立大学）；University of Rochester（罗切斯特大学）；Nanyang Technological University（南洋理工大学）；Nanjing University（南京大学）；WeNet Opensource Community（WeNet开源社区）；EURECOM
Adaptive Fake Audio Detection with Low-Rank Model Squeezing
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Jiaotong University（北京交通大学）；University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）
ADD 2022: the First Audio Deep Synthesis Detection Challenge
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）
Genuine-Focused Learning using Mask AutoEncoder for Generalized Fake Audio Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Tsinghua University（清华大学）；School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；Shanghai Polytechnic University（上海第二工业大学）
Mixture of Experts Fusion for Fake Audio Detection Using Frozen wav2vec 2.0
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）；AI Lab, Tencent（腾讯AI实验室）
ADD 2023: the Second Audio Deepfake Detection Challenge
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Generalized Fake Audio Detection via Deep Stable Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Chinese Academy of Sciences（中国科学院人工智能学院）；Tsinghua University（清华大学）；Communication University of China（中国传媒大学）；Shanghai Polytechnic University（上海第二工业大学）
Binghamton University, State University of New York（纽约州立大学宾汉姆顿大学）；Center for Collective Dynamics of Complex Systems（复杂系统集体动力学中心）；Department of Systems Science and Industrial Engineering（系统科学与工业工程系）；Bernard M. & Ruth R. Bass Center for Leadership Studies（伯纳德·M与露丝·R·巴斯领导力研究中心）
Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategy
Communication University of China（中国传媒大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
EURECOM（欧洲通信研究院）；National Institute of Informatics（国立信息学研究所）；University of Eastern Finland（东芬兰大学）；Inria（法国国家信息与自动化研究所）；University of Edinburgh（爱丁堡大学）；NEC Corp.（日本电气股份有限公司）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
