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    題名: Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning
    作者: 廖文宏
    Liao, Wen-Hung;Khan, Sarwar;Chen, Jun-Cheng;Chen, Chu-Song
    貢獻者: 資訊系
    關鍵詞: Adversarial attack;Adversarial training;Deepfake video detection;Forgery detector
    日期: 2024-01
    上傳時間: 2025-01-07 09:36:39 (UTC+8)
    摘要: Deepfake technology has raised concerns about the authenticity of digital content, necessitating the development of effective detection methods. However, the widespread availability of deepfakes has given rise to a new challenge in the form of adversarial attacks. Adversaries can manipulate deepfake videos with small, imperceptible perturbations that can deceive the detection models into producing incorrect outputs. To tackle this critical issue, we introduce Adversarial Feature Similarity Learning (AFSL), which integrates three fundamental deep feature learning paradigms. By optimizing the similarity between samples and weight vectors, our approach aims to distinguish between real and fake instances. Additionally, we aim to maximize the similarity between both adversarially perturbed examples and unperturbed examples, regardless of their real or fake nature. Moreover, we introduce a regularization technique that maximizes the dissimilarity between real and fake samples, ensuring a clear separation between these two categories. With extensive experiments on popular deepfake datasets, including FaceForensics++, FaceShifter, and DeeperForensics, the proposed method outperforms other standard adversarial training-based defense methods significantly. This further demonstrates the effectiveness of our approach to protecting deepfake detectors from adversarial attacks.
    關聯: Proceedings of the 30th International Conference on Multimedia Modeling, pp.503-516, University of Amsterdam
    資料類型: conference
    DOI 連結: https://doi.org/10.1007/978-3-031-53311-2_37
    DOI: 10.1007/978-3-031-53311-2_37
    顯示於類別:[資訊科學系] 會議論文

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