An adversarial attack approach for eXplainable AI evaluation on deepfake detection models-Reference-Cited by-同舟云学术

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An adversarial attack approach for eXplainable AI evaluation on deepfake detection models

Published:2024-04 Issue: Volume:139 Page:103684
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Gowrisankar Balachandar^ORCID,Thing Vrizlynn L.L.^ORCID

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference37 articles.

1. Afchar, D., Nozick, V., Yamagishi, J., Echizen I., 2018. MesoNet: a compact facial video forgery detection network, 2018 IEEE International Workshop On Information Forensics and Security (WIFS), Hong Kong, China, pp. 1–7, doi:10.1109/WIFS.2018.863076.

2. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation;Bach;PLoS ONE,2015

3. Cai, Z., Stefanov, K., Dhall, A., Hayat, M., 2022. Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization, [Online]. Available: https://arxiv.org/abs/2204.06228.

4. Chollet, F., 2017. Xception: deep Learning with Depthwise Separable Convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 1800–1807, doi:10.1109/CVPR.2017.195.

5. Coccomini, D.A., Messina, N., Gennaro, C., Falchi, F. (2022). Combining EfficientNet and Vision Transformers for Video Deepfake Detection. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13233. Springer, Cham. https://doi.org/10.1007/978-3-031-06433-3_19.

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1. Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection;3rd ACM International Workshop on Multimedia AI against Disinformation;2024-06-10

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