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Zijin Yang

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4 papers

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AAAI Conference 2026 Conference Paper

AEDR: Training-Free AI-Generated Image Attribution via Autoencoder Double-Reconstruction

Chao Wang
Zijin Yang
Yaofei Wang
Weiming Zhang
Kejiang Chen

The rapid advancement of image-generation technologies has made it possible for anyone to create photorealistic images using generative models, raising significant security concerns. To mitigate malicious use, tracing the origin of such images is essential. Reconstruction-based attribution methods offer a promising solution, but they often suffer from reduced accuracy and high computational costs when applied to state‑of‑the‑art (SOTA) models. To address these challenges, we propose AEDR (AutoEncoder Double-Reconstruction), a novel training‑free attribution method designed for generative models with continuous autoencoders. Unlike existing reconstruction‑based approaches that rely on the value of a single reconstruction loss, AEDR performs two consecutive reconstructions using the model’s autoencoder, and adopts the ratio of these two reconstruction losses as the attribution signal. This signal is further calibrated using the image homogeneity metric to improve accuracy, which inherently cancels out absolute biases caused by image complexity, with autoencoder‑based reconstruction ensuring superior computational efficiency. Experiments on eight top latent diffusion models show that AEDR achieves 25.5% higher attribution accuracy than existing reconstruction‑based methods, with requiring only 1% of the computational time.

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AAAI Conference 2025 Conference Paper

CoSDA: Enhancing the Robustness of Inversion-based Generative Image Watermarking Framework

Han Fang
Kejiang Chen
Zijin Yang
Bosen Cui
Weiming Zhang
Ee-Chien Chang

Generative image watermarking inserts secret watermarks into generated images and plays an important role in tracing the usages of generative models. For watermarking of diffusion models, inversion-based framework emerges as an effective approach. Such framework employs a robust mechanism to embed the watermark into the starting latent before ``forward sampling'', thereby generating images with the implicit watermark. During watermark detection, inversion techniques are employed to reverse the process and obtain the watermarked latent, followed by further extraction. The robustness of this technique hinges primarily on the embedding mechanism and inversion accuracy. Previous methods predominantly focused on enhancing the robustness of the embedding mechanism but overlooked the reduction of the inversion errors. However, our results show that inversion error will significantly affect the overall robustness. Therefore, in this paper, we delve into the inversion error aspect and propose CoSDA, a compensation sampling and drift alignment-based approach. The inversion error primarily accumulated during two stages: the internal error incurred by the algorithm, and the inevitable external noise. We observe that the main source of internal error comes from the mismatch in conditions (e.g. prompt, guidance scale) between forward and backward sampling processes. Therefore, we propose a compensation-based forward sampling, compensating for certain mismatch conditions and reducing the inversion error caused by the mismatch. Addressing external error caused by inevitable image distortions (e.g. JPEG compression), we introduce a drift-alignment approach, where a neural network is trained adversarially to restore the original watermarked latent from the distorted counterpart. Experimental results show that CoSDA effectively enhances watermark robustness while maintaining the visual quality of generated images.

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AAAI Conference 2025 Conference Paper

Provably Secure Image Robust Steganography via Cross-modal Error Correction

Yuang Qi
Kejiang Chen
Na Zhao
Zijin Yang
Weiming Zhang

The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably secure image steganography. However, existing methods face issues such as low quality of generated images and lack of semantic control in the generation process. To leverage provably secure steganography with more effective and high-performance image generation models, and to ensure that stego images can accurately extract secret messages even after being uploaded to social networks and subjected to lossy processing such as JPEG compression, we propose a high-quality, provably secure, and robust image steganography method based on state-of-the-art autoregressive (AR) image generation models using Vector-Quantized (VQ) tokenizers. Additionally, we employ a cross-modal error-correction framework that generates stego text from stego images to aid in restoring lossy images, ultimately enabling the extraction of secret messages embedded within the images. Extensive experiments have demonstrated that the proposed method provides advantages in stego quality, embedding capacity, and robustness, while ensuring provable undetectability.

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NeurIPS Conference 2025 Conference Paper

StegoZip: Enhancing Linguistic Steganography Payload in Practice with Large Language Models

Jun Jiang
Zijin Yang
Weiming Zhang
Nenghai Yu
Kejiang Chen

Generative steganography has emerged as an active research area, yet its practical system is constrained by the inherent secret payload limitation caused by low entropy in generating stego texts. This payload limitation necessitates the use of lengthy stego texts or frequent transmissions, which increases the risk of suspicion by adversaries. Previous studies have mainly focused on payload enhancement through optimized entropy utilization while overlooking the crucial role of secret message processing. To address this gap, we propose StegoZip, a framework that leverages large language models to optimize secret message processing. StegoZip consists of two core components: semantic redundancy pruning and index-based compression coding. The former dynamically prunes the secret message to extract a low-semantic representation, whereas the latter further compresses it into compact binary codes. When integrated with state-of-the-art steganographic methods under lossless decoding, StegoZip achieves 2. 5$\times$ the payload of the baselines while maintaining comparable processing time in practice. This enhanced payload significantly improves covertness by mitigating the risks associated with frequent transmissions while maintaining provable content security.

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