StegaFFD：通过细粒度隐写域提升实现隐私保护的面部伪造检测
StegaFFD: Privacy-Preserving Face Forgery Detection via Fine-Grained Steganographic Domain Lifting
AI总结：
StegaFFD通过隐写域提升实现隐私保护的面部伪造检测，利用低频感知分解和空间频率差分注意力来增强隐蔽特征的感知，同时保持检测准确性。
摘要：
大多数现有的面部伪造检测（FFD）模型假设可以访问原始面部图像。在实践中， 在客户端-服务器框架下，私有面部数据可能在传输过程中被拦截或由不可信的服务器泄露。先前的隐私保护方法，如匿名化、加密或扭曲，部分减轻了泄漏，但往往引入严重的语义扭曲，使图像看起来明显被保护。这会引发攻击者更加激进的策略，使过程变成猫和老鼠的游戏。此外，这些方法严重操纵图像内容，引入降解或伪影，可能混淆依赖于极细微伪造痕迹的FFD模型。受图像隐写术的进步启发，该方法能够实现高保真的隐藏和恢复，我们提出了一种基于隐写术的面部伪造检测框架（StegaFFD）以在不引起怀疑的情况下保护隐私。StegaFFD将面部图像隐藏在自然覆盖图像中，并直接在隐写域中进行伪造检测。然而，隐藏的伪造特定特征非常微妙，受到覆盖语义的干扰，带来了显著的挑战。为此，我们提出了低频感知分解（LFAD）和空间频率差分注意力（SFDA），以抑制来自低频覆盖语义的干扰，并增强隐藏面部特征的感知。此外，我们引入了隐写域对齐（SDA）以对齐隐藏面部的表示与它们的原始对应物的表示，增强模型在隐写域中感知细微面部线索的能力。在七个FFD数据集上的广泛实验表明，StegaFFD实现了强大的不可察觉性，避免了引起攻击者怀疑，并在现有面部隐私保护方法中更好地保持FFD准确性。
英文摘要：
Most existing Face Forgery Detection (FFD) models assume access to raw face images. In practice, under a client-server framework, private facial data may be intercepted during transmission or leaked by untrusted servers. Previous privacy protection approaches, such as anonymization, encryption, or distortion, partly mitigate leakage but often introduce severe semantic distortion, making images appear obviously protected. This alerts attackers, provoking more aggressive strategies and turning the process into a cat-and-mouse game. Moreover, these methods heavily manipulate image contents, introducing degradation or artifacts that may confuse FFD models, which rely on extremely subtle forgery traces. Inspired by advances in image steganography, which enable high-fidelity hiding and recovery, we propose a Stega}nography-based Face Forgery Detection framework (StegaFFD) to protect privacy without raising suspicion. StegaFFD hides facial images within natural cover images and directly conducts forgery detection in the steganographic domain. However, the hidden forgery-specific features are extremely subtle and interfered with by cover semantics, posing significant challenges. To address this, we propose Low-Frequency-Aware Decomposition (LFAD) and Spatial-Frequency Differential Attention (SFDA), which suppress interference from low-frequency cover semantics and enhance hidden facial feature perception. Furthermore, we introduce Steganographic Domain Alignment (SDA) to align the representations of hidden faces with those of their raw counterparts, enhancing the model's ability to perceive subtle facial cues in the steganographic domain. Extensive experiments on seven FFD datasets demonstrate that StegaFFD achieves strong imperceptibility, avoids raising attackers' suspicion, and better preserves FFD accuracy compared to existing facial privacy protection methods.
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