标题： 面向不可察觉的对抗防御：一种针对面部操作的梯度驱动防护罩 显示英文标题

标题： Towards Imperceptible Adversarial Defense: A Gradient-Driven Shield against Facial Manipulations

摘要： 随着生成模型的繁荣发展，被操纵的面部图像变得越来越容易获取，引发了关于隐私侵犯和社会信任的问题。 为应对这一问题，主动防御策略将对抗性扰动嵌入面部图像以对抗深度伪造操作。 然而，现有方法常常在不可察觉性和防御效果之间面临权衡——强扰动可能会破坏伪造内容，但会降低视觉保真度。 最近的研究尝试通过引入额外的视觉损失约束来解决这个问题，但往往忽视了损失之间的潜在梯度冲突，最终削弱了防御性能。 为了弥补这一差距，我们提出了一种基于梯度投影的对抗性主动防御（GRASP）方法，在有效对抗面部深度伪造的同时最小化感知退化。 GRASP是首个成功结合结构相似性损失和低频损失以增强扰动不可察觉性的方法。 通过分析防御效果损失与视觉质量损失之间的梯度冲突，GRASP开创性地设计了梯度投影机制以缓解这些冲突，实现了平衡优化，在不牺牲防御性能的情况下保持图像保真度。 大量实验验证了GRASP的有效性，达到了超过40 dB的PSNR，0.99的SSIM，以及针对面部属性操纵100%的防御成功率，在视觉质量方面显著优于现有方法。 显示英文摘要

摘要： With the flourishing prosperity of generative models, manipulated facial images have become increasingly accessible, raising concerns regarding privacy infringement and societal trust. In response, proactive defense strategies embed adversarial perturbations into facial images to counter deepfake manipulation. However, existing methods often face a tradeoff between imperceptibility and defense effectiveness-strong perturbations may disrupt forgeries but degrade visual fidelity. Recent studies have attempted to address this issue by introducing additional visual loss constraints, yet often overlook the underlying gradient conflicts among losses, ultimately weakening defense performance. To bridge the gap, we propose a gradient-projection-based adversarial proactive defense (GRASP) method that effectively counters facial deepfakes while minimizing perceptual degradation. GRASP is the first approach to successfully integrate both structural similarity loss and low-frequency loss to enhance perturbation imperceptibility. By analyzing gradient conflicts between defense effectiveness loss and visual quality losses, GRASP pioneers the design of the gradient-projection mechanism to mitigate these conflicts, enabling balanced optimization that preserves image fidelity without sacrificing defensive performance. Extensive experiments validate the efficacy of GRASP, achieving a PSNR exceeding 40 dB, SSIM of 0.99, and a 100% defense success rate against facial attribute manipulations, significantly outperforming existing approaches in visual quality.