The proliferation of generative image models has revolutionized AIGC creation while amplifying concerns over content provenance and manipulation forensics. Existing methods are typically either unable to localize tampering or restricted to specific generative settings, limiting their practical utility. We propose GenPTW, a General watermarking framework that unifies Provenance tracing and Tamper localization in latent space. It supports both in-generation and post-generation embedding without altering the generative process, and is plug-and-play compatible with latent diffusion models (LDMs) and visual autoregressive (VAR) models. To achieve precise provenance tracing and tamper localization, we embed the watermark using two complementary mechanisms: cross-attention fusion aligned with latent semantics and spatial fusion providing explicit spatial guidance for edit sensitivity. A tamper-aware extractor jointly conducts provenance tracing and tamper localization by leveraging watermark features together with high-frequency features. Experiments show that GenPTW maintains high visual fidelity and strong robustness against diverse AIGC-editing.

Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused. In this study, we empirically show that different timesteps of DDIM inversion reveal varying subtle distinctions between synthetic and real images that are extractable for detection, taking the forms of such as Fourier power spectrum high-frequency discrepancies and inter-pixel variance distributions. Based on these observations, we propose a novel detection method named ESIDE that directly utilizes features of intermediately noised images by training an ensemble on multiple noised timesteps, circumventing the overtime of conventional reconstruction-based strategies. To enhance human comprehension, we introduce a metric-grounded explanation refinement module to identify and explain AI-generated flaws. Additionally, we present the benchmarks GenHard and GenExplain, offering detection samples of greater difficulty and high-quality rationales for fake images. Extensive experiments show that ESIDE achieves state-of-the-art performance with 98.91% and 95.89% detection accuracy on regular and challenging samples respectively, and demonstrates generalizability and robustness.

Audio watermarking has been widely applied in copyright protection and source tracing. However, due to the inherent characteristics of audio signals, watermark localization and resistance to desynchronization attacks remain significant challenges. In this paper, we propose a learning-based scheme named SyncGuard to address these challenges. Specifically, we design a frame-wise broadcast embedding strategy to embed the watermark in arbitrary-length audio, enhancing time-independence and eliminating the need for localization during watermark extraction. To further enhance robustness, we introduce a meticulously designed distortion layer. Additionally, we employ dilated residual blocks in conjunction with dilated gated blocks to effectively capture multi-resolution time-frequency features. Extensive experimental results show that SyncGuard efficiently handles variable-length audio segments, outperforms state-of-the-art methods in robustness against various attacks, and delivers superior auditory quality.

Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process. Although existing traditional video steganography methods excel in balancing security and capacity, they lack adequate robustness against common distortions in online social networks (OSNs). In this paper, we propose an end-to-end robust generative video steganography network (RoGVSN), which achieves visual editing by modifying semantic feature of videos to embed secret message. We exemplify the face-swapping scenario as an illustration to demonstrate the visual editing effects. Specifically, we devise an adaptive scheme to seamlessly embed secret messages into the semantic features of videos through fusion blocks. Extensive experiments demonstrate the superiority of our method in terms of robustness, extraction accuracy, visual quality, and capacity.

Although supervised deep learning methods achieve favorable performance in automatic sleep staging, they are limited in clinical situations due to the heavy reliance on massive labeled multi-channel polysomnogram (PSG) recordings. Accordingly, to alleviate the reliance on labeled PSG, we present SleepECL, a self-supervised learning framework based on electroencephalogram (EEG) signals taking advantage of contrastive learning, which learns efficient representations by contrasting semantically consistent and inconsistent samples (a.k.a. positive samples and negative samples). Specifically, SleepECL conducts contrastive learning upon local representations (i.e., intra-epoch EEG decoding) as well as contextual representations (i.e., interepoch dependency) and incorporates sleep expert knowledge to discover more accurate positive samples in contrastive learning, leading to more effective representations. Experimental results on two publicly available datasets demonstrate that our SleepECL outperforms state-of-the-art self-supervised methods. Moreover, the pre-trained model achieves acceptable performance using only a few label single-channel EEG recordings, which contributes to a more convenient application of automatic sleep staging in clinical situations.