{"type":"ScholarlyArticle","title":"Towards Robust Gan-Generated Image Detection: A Multi-View Completion Representation","authors":[{"name":"Chi Liu","slug":"chi-liu","given":"Chi","family":"Liu","affiliation":"School of Computer Science , University of Technology Sydney , Australia"},{"name":"Tianqing Zhu","slug":"tianqing-zhu","given":"Tianqing","family":"Zhu","affiliation":"School of Computer Science , University of Technology Sydney , Australia"},{"name":"Sheng Shen","slug":"sheng-shen","given":"Sheng","family":"Shen","affiliation":"School of Electrical and Information Engineering , The University of Sydney , Australia"},{"name":"Wanlei Zhou","slug":"wanlei-zhou","given":"Wanlei","family":"Zhou","affiliation":"City University of Macau , Macao SAR , China"}],"abstract":"GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. Although some existing detectors work well in detecting clean, known GAN samples, their success is largely attributable to overfitting unstable features such as frequency artifacts, which will cause failures when facing unknown GANs or perturbation attacks. To overcome the issue, we propose a robust detection framework based on a novel multi-view image completion representation. The framework first learns various view-to-image tasks to model the diverse distributions of genuine images. Frequency-irrelevant features can be represented from the distributional discrepancies characterized by the completion models, which are stable, generalized, and robust for detecting unknown fake patterns. Then, a multi-view classification is devised with elaborated intra- and inter-view learning strategies to enhance view-specific feature representation and cross-view feature aggregation, respectively. We evaluated the generalization ability of our framework across six popular GANs at different resolutions and its robustness against a broad range of perturbation attacks. The results confirm our method's improved effectiveness, generalization, and robustness over various baselines.","doi":"10.24963/ijcai.2023/52","url":"https://ijcai.org/proceedings/2023/52","pdf":"https://ijcai.org/proceedings/2023/0052.pdf","markdown":"https://ijcai.org/proceedings/2023/52.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/52","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":52,"pages":{"first":464,"last":472},"keywords":["Multidisciplinary Topics and Applications: MDA: Security and privacy","Computer Vision: CV: Neural generative models, auto encoders, GANs","AI Ethics, Trust, Fairness: ETF: Safety and robustness"],"videos":[],"references":[{"position":1,"raw":"[Barni et al., 2020] Mauro Barni, Kassem Kallas, Ehsan Nowroozi, and Benedetta Tondi. Cnn detection of gan- generated face images based on cross-band co-occurrences analysis. In 2020 IEEE international workshop on infor- mation forensics and security (WIFS), pages 1-6. IEEE, 2020.","title":"Cnn detection of gangenerated face images based on cross-band co-occurrences analysis","authors":"Mauro Barni, Kassem Kallas, Ehsan Nowroozi, Benedetta Tondi","year":"2020","venue":"IEEE international workshop on information forensics and security (WIFS)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bellemare et al., 2017] Marc G Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos. The cramer distance as a solution to biased wasserstein gradients. arXiv preprint arXiv:1705.10743, 2017.","title":"The cramer distance as a solution to biased wasserstein gradients","authors":"Ivo Marc G Bellemare, Will Danihelka, Shakir Dabney, Balaji Mohamed, Stephan Lakshminarayanan, Rémi Hoyer, Munos","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chollet, 2017] Franc ¸ois Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017.","title":"Franc ¸ois Chollet. Xception: Deep learning with depthwise separable convolutions","authors":"Chollet","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dong et al., 2022] Chengdong Dong, Ajay Kumar, and Eryun Liu. Think twice before detecting gan-generated fake images from their spectral domain imprints. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7865-7874, 2022.","title":"Think twice before detecting gan-generated fake images from their spectral domain imprints","authors":"Chengdong Dong, Ajay Kumar, Eryun Liu","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Durall et al., 2020] Ricard Durall, Margret Keuper, and Ja- nis Keuper. Watch your up-convolution: Cnn based gener- ative deep neural networks are failing to reproduce spectral distributions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7890- 7899, 2020.","title":"Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions","authors":"Ricard Durall, Margret Keuper, Janis Keuper","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dzanic et al., 2020] Tarik Dzanic, Karan Shah, and Freddie Witherden. Fourier spectrum discrepancies in deep net- work generated images. Advances in neural information processing systems, 33:3022-3032, 2020.","title":"Fourier spectrum discrepancies in deep network generated images","authors":"Tarik Dzanic, Karan Shah, Freddie Witherden","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Frank et al., 2020] Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake im- age recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020.","title":"Leveraging frequency analysis for deep fake image recognition","authors":"Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz","year":"2020","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goodfellow et al., 2014a] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. Advances in neural information processing systems, 27, 2014.","title":"Generative adversarial nets","authors":"Goodfellow","year":"2014","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Goodfellow et al., 2014b] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.","title":"Explaining and harnessing adversarial examples","authors":"Goodfellow","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2021] Yang He, Ning Yu, Margret Keuper, and Mario Fritz. Beyond the spectrum: Detecting deepfakes via re-synthesis. In Thirtieth International Joint Confer- ence on Artificial Intelligence, pages 2534-2541. IJCAI, 2021.","title":"Beyond the spectrum: Detecting deepfakes via re-synthesis","authors":"Yang He, Ning Yu, Margret Keuper, Mario Fritz","year":"2021","venue":"Thirtieth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2021/349"},{"position":11,"raw":"[He et al., 2022] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked au- toencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 16000-16009, 2022.","title":"Masked autoencoders are scalable vision learners","authors":"He","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hu et al., 2021] Shu Hu, Yuezun Li, and Siwei Lyu. Expos- ing gan-generated faces using inconsistent corneal specu- lar highlights. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2500-2504. IEEE, 2021.","title":"Exposing gan-generated faces using inconsistent corneal specular highlights","authors":"Shu Hu, Yuezun Li, Siwei Lyu","year":"2021","venue":"ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2020] Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, and Geguang Pu. Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruc- tion. In Proceedings of the 28th ACM international con- ference on multimedia, pages 1217-1226, 2020.","title":"Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction","authors":"Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu","year":"2020","venue":"Proceedings of the 28th ACM international conference on multimedia","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jeon et al., 2020] Hyeonseong Jeon, Youngoh Bang, and Si- mon S Woo. Fdftnet: Facing off fake images using fake detection fine-tuning network. In IFIP international con- ference on ICT systems security and privacy protection, pages 416-430. Springer, 2020.","title":"Simon S Woo. Fdftnet: Facing off fake images using fake detection fine-tuning network","authors":"Jeon","year":"2020","venue":"IFIP international conference on ICT systems security and privacy protection","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jeong et al., 2022a] Yonghyun Jeong, Doyeon Kim, Seung- jai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Con- ference on Applications of Computer Vision, pages 48-57, 2022.","title":"Bihpf: Bilateral high-pass filters for robust deepfake detection","authors":"Jeong","year":"2022","venue":"Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jeong et al., 2022b] Yonghyun Jeong, Doyeon Kim, Young- min Ro, and Jongwon Choi. Frepgan: Robust deepfake detection using frequency-level perturbations. In Pro- ceedings of the AAAI conference on artificial intelligence, 2022.","title":"Frepgan: Robust deepfake detection using frequency-level perturbations","authors":"Jeong","year":"2022","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jung and Keuper, 2021] Steffen Jung and Margret Keuper. Spectral distribution aware image generation. In Proceed- ings of the AAAI conference on artificial intelligence, vol- ume 35, pages 1734-1742, 2021.","title":"Spectral distribution aware image generation","authors":"Jung, Keuper, Steffen Jung, Margret Keuper","year":"2021","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Karras et al., 2018] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for im- proved quality, stability, and variation. In International Conference on Learning Representations, 2018.","title":"Progressive growing of gans for improved quality, stability, and variation","authors":"Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Karras et al., 2019] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019.","title":"A style-based generator architecture for generative adversarial networks","authors":"Karras","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Karras et al., 2020] Tero Karras, Samuli Laine, Miika Ait- tala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. An- alyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 8110-8119, 2020.","title":"Analyzing and improving the image quality of stylegan","authors":"Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kingma and Ba, 2015] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR (Poster), 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":"ICLR (Poster)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2017] Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos. Mmd gan: Towards deeper understanding of moment matching network. Advances in neural information processing systems, 30, 2017.","title":"Mmd gan: Towards deeper understanding of moment matching network","authors":"Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2015] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015.","title":"Deep learning face attributes in the wild","authors":"Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2015","venue":"Proceedings of International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2020] Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr. Global texture enhancement for fake face detection in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8060- 8069, 2020.","title":"Global texture enhancement for fake face detection in the wild","authors":"Liu","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liu et al., 2021] Honggu Liu, Xiaodan Li, Wenbo Zhou, Yuefeng Chen, Yuan He, Hui Xue, Weiming Zhang, and Nenghai Yu. Spatial-phase shallow learning: rethinking face forgery detection in frequency domain. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pages 772-781, 2021.","title":"Spatial-phase shallow learning: rethinking face forgery detection in frequency domain","authors":"Honggu Liu, Xiaodan Li, Wenbo Zhou, Yuefeng Chen, Yuan He, Hui Xue, Weiming Zhang, Nenghai Yu","year":"2021","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2022] Chi Liu, Huajie Chen, Tianqing Zhu, Jun Zhang, and Wanlei Zhou. Making deepfakes more spuri- ous: evading deep face forgery detection via trace removal attack. arXiv preprint arXiv:2203.11433, 2022.","title":"Making deepfakes more spurious: evading deep face forgery detection via trace removal attack","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Madry et al., 2018] Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. Towards deep learning models resistant to adversarial attacks. In International Conference on Learning Representations, 2018.","title":"Towards deep learning models resistant to adversarial attacks","authors":"Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Marra et al., 2018] Francesco Marra, Diego Gragnaniello, Davide Cozzolino, and Luisa Verdoliva. Detection of gan- generated fake images over social networks. In 2018 IEEE conference on multimedia information processing and re- trieval (MIPR), pages 384-389. IEEE, 2018.","title":"Detection of gangenerated fake images over social networks","authors":"Francesco Marra, Diego Gragnaniello, Davide Cozzolino, Luisa Verdoliva","year":"2018","venue":"IEEE conference on multimedia information processing and retrieval (MIPR)","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Marra et al., 2019a] Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. Do gans leave artifi- cial fingerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pages 506- 511. IEEE, 2019.","title":"Do gans leave artificial fingerprints?","authors":"Marra","year":"2019","venue":"IEEE conference on multimedia information processing and retrieval (MIPR)","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Marra et al., 2019b] Francesco Marra, Cristiano Saltori, Giulia Boato, and Luisa Verdoliva. Incremental learning for the detection and classification of gan-generated im- ages. In 2019 IEEE international workshop on informa- tion forensics and security (WIFS), pages 1-6. IEEE, 2019.","title":"Incremental learning for the detection and classification of gan-generated images","authors":"Marra","year":"2019","venue":"IEEE international workshop on information forensics and security (WIFS)","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[McCloskey and Albright, 2019] Scott McCloskey and Michael Albright. Detecting gan-generated imagery using saturation cues. In 2019 IEEE international conference on image processing (ICIP), pages 4584-4588. IEEE, 2019.","title":"Detecting gan-generated imagery using saturation cues","authors":"Scott Mccloskey, Michael Albright","year":"2019","venue":"IEEE international conference on image processing (ICIP)","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Mi et al., 2020] Zhongjie Mi, Xinghao Jiang, Tanfeng Sun, and Ke Xu. Gan-generated image detection with self-attention mechanism against gan generator defect. IEEE Journal of Selected Topics in Signal Processing, 14(5):969-981, 2020.","title":"Gan-generated image detection with self-attention mechanism against gan generator defect","authors":"Zhongjie Mi, Xinghao Jiang, Tanfeng Sun, Ke Xu","year":"2020","venue":"IEEE Journal of Selected Topics in Signal Processing","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Miyato et al., 2018] Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. Spectral nor- malization for generative adversarial networks. In International Conference on Learning Representations, 2018.","title":"Spectral normalization for generative adversarial networks","authors":"Miyato","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Nataraj et al., 2019] Lakshmanan Nataraj, Tajuddin Manhar Mohammed, BS Manjunath, Shivkumar Chandrasekaran, Arjuna Flenner, Jawadul H Bappy, and Amit K Roy- Chowdhury. Detecting gan generated fake images using co-occurrence matrices. Electronic Imaging, 2019(5):532-1, 2019.","title":"Detecting gan generated fake images using co-occurrence matrices","authors":"Lakshmanan Nataraj, Manhar Tajuddin, B S Mohammed, Shivkumar Manjunath, Arjuna Chandrasekaran, Flenner, H Jawadul, Amit K Roy-Chowdhury Bappy","year":"2019","venue":"Electronic Imaging","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Qian et al., 2020] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020.","title":"Thinking in frequency: Face forgery detection by mining frequency-aware clues","authors":"Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, Jing Shao","year":"2020","venue":"European conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Ronneberger et al., 2015] Olaf Ronneberger, Philipp Fis- cher, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer- assisted intervention, pages 234-241. Springer, 2015.","title":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation","authors":"Ronneberger","year":"2015","venue":"International Conference on Medical image computing and computerassisted intervention","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Ruff et al., 2020] Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, and Marius Kloft. Deep semi- supervised anomaly detection. In International Confer- ence on Learning Representations, 2020.","title":"Deep semisupervised anomaly detection","authors":"Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft","year":"2020","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Tariq et al., 2018] Shahroz Tariq, Sangyup Lee, Hoyoung Kim, Youjin Shin, and Simon S Woo. Detecting both ma- chine and human created fake face images in the wild. In Proceedings of the 2nd international workshop on multi- media privacy and security, pages 81-87, 2018.","title":"Detecting both machine and human created fake face images in the wild","authors":"Tariq","year":"2018","venue":"Proceedings of the 2nd international workshop on multimedia privacy and security","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2020] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn- generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020.","title":"Cnngenerated images are surprisingly easy to spot... for now","authors":"Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A Efros","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang et al., 2022] Junke Wang, Zuxuan Wu, Wenhao Ouyang, Xintong Han, Jingjing Chen, Yu-Gang Jiang, and Ser-Nam Li. M2tr: Multi-modal multi-scale transform- ers for deepfake detection. In Proceedings of the 2022 International Conference on Multimedia Retrieval, pages 615-623, 2022.","title":"M2tr: Multi-modal multi-scale transformers for deepfake detection","authors":"Wang","year":"2022","venue":"Proceedings of the 2022 International Conference on Multimedia Retrieval","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yu et al., 2019] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF interna- tional conference on computer vision, pages 7556-7566, 2019.","title":"Attributing fake images to gans: Learning and analyzing gan fingerprints","authors":"Ning Yu, Larry S Davis, Mario Fritz","year":"2019","venue":"Proceedings of the IEEE/CVF international conference on computer vision","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2019] Xu Zhang, Svebor Karaman, and Shih- Fu Chang. Detecting and simulating artifacts in gan fake images. In 2019 IEEE international workshop on infor- mation forensics and security (WIFS), pages 1-6. IEEE, 2019.","title":"Detecting and simulating artifacts in gan fake images","authors":"Xu Zhang, Svebor Karaman, Shih-Fu Chang","year":"2019","venue":"IEEE international workshop on information forensics and security (WIFS)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}