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.

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.

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.

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.

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. However, existing methods often compromise the model performance or require additional training, which is undesirable for operators and users. To address this issue, we propose Gaussian Shading, a diffusion model watermarking technique that is both performance-lossless and training-free, while serving the dual purpose of copyright protection and tracing of offending content. Our watermark embedding is free of model parameter modifications and thus is plug-and-play. We map the watermark to latent representations following a standard Gaussian distribution, which is indistinguishable from latent representations obtained from the non-watermarked diffusion model. Therefore we can achieve watermark embedding with lossless performance, for which we also provide theoretical proof Furthermore, since the watermark is intricately linked with image semantics, it exhibits resilience to lossy processing and erasure attempts. The watermark can be extracted by Denoising diffusion Implicit Models (DDIM) inversion and inverse sampling. We evaluate Gaussian Shading on multiple versions of Stable Diffusion, and the results demonstrate that Gaussian Shading not only is performance-lossless but also out-performs existing methods in terms of robustness.

The rapid development of image generative models has lowered the threshold for image creation but also raised security concerns related to the propagation of false information, urgently necessitating the development of detection technologies for AI-generated images. Presently, text-to-image generation stands as the predominant approach to image generation, where the rendering of generated images hinges on two primary factors: text prompts and the inherent characteristics of the model. However, the variety of semantic text prompts yields diverse generated images, posing significant challenges to existing detection methodologies that rely solely on learning from image features, particularly in scenarios with limited samples. To tackle these challenges, this paper presents a novel perspective on the AI-generated image detection task, advocating for detection under semantic-decoupling conditions. Building upon this insight, we propose SemGIR, a semantic-guided image regeneration based method for AI-generated image detection. SemGIR first regenerates images through image-to-text followed by a text-to-image generation process, subsequently utilizing these re-generated image pairs to derive discriminative features. This regeneration process effectively decouples semantic features organically, allowing the detection process to concentrate more on the inherent characteristics of the generative model. Such an efficient detection scheme can also be effectively applied to attribution. Experimental findings demonstrate that in realistic scenarios with limited samples, SemGIR achieves an average detection accuracy 15.76% higher than state-of-the-art (SOTA) methods. Furthermore, in attribution experiments on the SDv2.1 model, SemGIR attains an accuracy exceeding 98%, affirming the effectiveness and practical utility of the proposed method.

The maturity of generative models and the popularity of generated data have brought new technical means and camouflage environments to steganography. Numerous generative image steganography methods have emerged, but achieving provable security, robustness, and relatively high capacity simultaneously remains challenging. This paper proposes a provably secure robust image steganography method via the generative adversarial network (GAN), named PARIS. The sender maps the secret message, following a uniform distribution, to latent vectors conforming to a standard Gaussian distribution using inverse transform sampling. Subsequently, the latent vector is fed into the generator, producing the stego image. In this way, the stego image cannot be distinguished from the normally generated image. The receiver extracts the secret message from the recovered latent vector via gradient descent optimization. To enhance the robustness, a noise layer is introduced while recovering the latent vector to simulate potential lossy operations in real scenarios. The security of the proposed method is theoretically proven. Extensive experiments have also verified the proposed method's robustness, security, and relatively high capacity in terms of different GAN architectures, noises, and datasets.