Watermarking for diffusion images has drawn considerable attention due to the widespread use of text-to-image diffusion models and the increasing need for their copyright protection. Recently, advanced watermarking techniques, such as Tree-Ring, integrate watermarks by embedding traceable patterns (e.g., Rings) into the latent distribution during the diffusion process. Such methods disrupt the original semantics of the generated images due to the inevitable distribution shift caused by the watermarks, thereby limiting their practicality, particularly in digital art creation. In this work, we present Semantic-aware Pivotal Tuning Watermarks (PT-Mark), a novel invisible watermarking method that preserves both the semantics of diffusion images and the traceability of the watermark. PT-Mark preserves the original semantics of the watermarked image by gradually aligning the generation trajectory with the original (pivotal) trajectory while maintaining the traceable watermarks during whole diffusion denoising process. To achieve this, we first compute the salient regions of the watermark at each diffusion denoising step as a spatial prior to identify areas that can be aligned without disrupting the watermark pattern. Guided by the region, we then introduce an additional pivotal tuning branch that optimizes the null-text embedding to align the semantics while preserving the watermarks. Extensive evaluations demonstrate that PT-Mark can preserve the original semantics of the diffusion images while integrating robust watermarks. It achieves a 10% improvement in the performance of semantic preservation compared to state-of-the-art watermarking methods, while also showing comparable robustness against real-world perturbations and four times greater efficiency. The code is available athttps://github.com/annpion/PT-Mark.

Despite the promising advances in deepfake detection on current datasets, detecting visual deepfakes in real-world scenarios (e.g., deepfake videos and live streaming on YouTube) remains a challenge due to the inherent quality degradation such as unpredictable compression employed by social media platforms. Such degradation perturbs discernible forgery clues and diminishes the effectiveness of deepfake detection methods, raising a critical safety concern to the misuse of forgery faces in real-world scenarios. In this paper, we aim to understand the impacts of real-world degradation on the robustness of deepfake detection. Particularly, we investigate the risk of degraded deepfakes towards their detection on two real-world scenarios (i.e., deepfake videos and deepfake live streaming on social media platforms). By measuring the effects of real-world degradations on the performance and representation capabilities of detection models, we reveal that real-world deepfakes can be simulated via common degradation operations (e.g., JPEG compression) as they are perceptually similar to deepfake detectors. By analyzing the training dynamics under different sequences of training samples, we observe that the training order of deepfakes progressing from non-degraded (easy) to heavily degraded (hard) enhances the adaptability of detection models to various degradation in real-world scenarios. Drawing from these observations, we present a novel deepfake detection method ProFake to enhance the robustness of deepfake detection against real-world quality degradations. ProFake enables quality-adaptive learning via progressively degrade, detect and assign weights for the training samples driven by the feedback of model performance and image quality, which ensures that our model gradually focuses on more challenging samples to achieve quality-adaptive deepfake detection. Extensive experiments show that compared with existing methods, ProFake improves deepfake detection accuracy by an average of over 10 % in real-world scenarios and by an average of over 30 % in heavily degraded scenarios, while maintaining comparable performance in detecting high-quality deepfakes.

Recent research on vision-based tasks has achieved great improvement due to the development of deep learning solutions. However, deep models have been found vulnerable to adversarial attacks where the original inputs are maliciously manipulated and cause dramatic shifts to the outputs. In this paper, we focus on adversarial attacks in image classifiers built with deep neural networks and propose a model-agnostic approach to detect adversarial inputs. We argue that the logit semantics of adversarial inputs follow a different evolution with respect to original inputs, and construct a logits-based embedding of features for effective representation learning. We train an LSTM network to further analyze the sequence of logits-based features to detect adversarial examples. Experimental results on the MNIST, CFAR-10, and CFAR-100 datasets show that our method achieves state-of-the-art accuracy for detecting adversarial examples and has strong generalizability.