Deepfake technology, driven by Generative Adversarial Networks (GANs), poses significant risks to privacy and societal security. Existing detection methods are predominantly passive, focusing on post-event analysis without preventing attacks. To address this, we propose an active defense method based on low-frequency perceptual perturbations to disrupt face-swapping manipulation, reducing the performance and naturalness of generated content. Unlike prior approaches that used low-frequency perturbations to impact classification accuracy, our method directly targets the generative process of deepfake techniques.We combine frequency and spatial domain features to strengthen defenses. By introducing artifacts through low-frequency perturbations while preserving high-frequency details, we ensure the output remains visually plausible. Additionally, we design a complete architecture featuring an encoder, a perturbation generator, and a decoder, leveraging discrete wavelet transform (DWT) to extract low-frequency components and generate perturbations that disrupt facial manipulation models. Experiments on CelebA-HQ and LFW demonstrate significant reductions in face-swapping effectiveness, improved defense success rates, and preservation of visual quality.

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios.

Wavelength-dependent light absorption and scattering result in the color cast and contrast degradation, which degrades the visibility of underwater images. Deep-learning-based under-water image enhancement has made significant progress in recent years. However, existing algorithms rely heavily on massive data for training, and the difficulty of collecting massive data in real-world environments limits their applicability. To alleviate this problem, we propose a fast adaptive self-supervised underwater image enhancement method, which models the entire water domain in terms of color and contrast distribution with only a few images. Specifically, to learn the mapping of color correction and contrast enhancement, our framework contains two sub-networks: a Color Mapping Net (CLM-Net) and a Contrast Mapping Net (CTM-Net). The CLM-Net learns the color correction mapping under the constraint of gray world assumption in a self-supervised manner. Besides, the CTM-Net estimates the contrast enhancement mapping under the constraint of the dark channel prior. Thanks to adaptive self-supervised training, we can primarily alleviate the need for collecting massive data and achieve effective underwater image enhancement with only ten images. Experiments demonstrate that the proposed algorithm achieves state-of-the-art performance on four benchmark datasets.