Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

TL;DR

Gaussian Shading++ tackles the practical deployment challenges of watermarking diffusion-model outputs by introducing a double-channel latent design that fixes the watermark key while encoding a random seed via PRC LDPC codes. It models generation and inversion as an AWGN channel and uses soft decision decoding to achieve near-optimal robustness across varying guidance scales, enabling performance-lossless watermarking. The framework also enables third-party verification through ECDSA signatures, balancing security with transparency; theoretical IND-CPA security guarantees accompany extensive empirical demonstration of robustness and minimal perceptual degradation. The approach outperforms existing methods in both robustness to distortions and maintenance of latent-distribution fidelity, making it a viable solution for copyright protection, content-traceability, and trustworthy model deployment in real-world diffusion pipelines.

Abstract

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. Existing methods primarily focus on ensuring that watermark embedding does not degrade the model performance. However, they often overlook critical challenges in real-world deployment scenarios, such as the complexity of watermark key management, user-defined generation parameters, and the difficulty of verification by arbitrary third parties. To address this issue, we propose Gaussian Shading++, a diffusion model watermarking method tailored for real-world deployment. We propose a double-channel design that leverages pseudorandom error-correcting codes to encode the random seed required for watermark pseudorandomization, achieving performance-lossless watermarking under a fixed watermark key and overcoming key management challenges. Additionally, we model the distortions introduced during generation and inversion as an additive white Gaussian noise channel and employ a novel soft decision decoding strategy during extraction, ensuring strong robustness even when generation parameters vary. To enable third-party verification, we incorporate public key signatures, which provide a certain level of resistance against forgery attacks even when model inversion capabilities are fully disclosed. Extensive experiments demonstrate that Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness, making it a more practical solution for real-world deployment.

Figures (8)

• Figure 1: Existing watermarking frameworks can be divided into three categories: (a) post-processing-based, (b) fine-tuning-based, and (c) latent-representation-based. Since methods (a) and (b) either introduce watermark residuals or require additional computational overhead, method (c) has emerged as the mainstream approach by overcoming these two drawbacks. Their performance is primarily evaluated based on the impact on the distribution.
• Figure 2: The two application scenarios of Gaussian Shading++ are Operator Verification and Third-party Verification. In the Operator Verification scenario, Gaussian Shading++ considers satisfying the requirements of generated image detection (copyright protection) and malicious user traceability. In the Third-party Verification scenario, Gaussian Shading++ considers the need for any third party to verify the watermark, and it aims to defend against the reprompt forgery attack that may emerge once model inversion capabilities become publicly available.
• Figure 3: The framework of Gaussian Shading++. The latent space is divided into the PRC Channel and GS Channel. During the watermark key generation, a ternary key set is generated for the PRC Channel. In the Third-party Verification scenario, a public-key signature ECDSA ECDSA key pair is introduced. During the watermark embedding, the PRC Channel serves as the header, encoding the random $seed$ to drive a PRNG, resulting in $m_{prc}$ . The GS Channel embeds a $k$-bit watermark sequence $s$, which undergoes diffusion, encryption, and transformation to produce $m_{gs}$. The combined sequence of $m_{prc}$ and $m_{gs}$ is used to drive distribution-preserving sampling, followed by denoising to generate watermarked images $X^s$. For watermark extraction, the process begins with Exact Inversion Hong_2024_CVPR to recover $z'_{T}$. The distortion throughout the entire generation and inversion process is modeled as an AWGN channel, enabling posterior estimation of the symbols of $z'_{T}$. Subsequently, the PRC Channel is first decoded to retrieve the random $seed$, which generates $K'$. $K'$ is employed to decrypt the GS Channel, and the final watermark is obtained through soft-decision decoding. In the Third-party Verification scenario, $s$ includes the user information and its signature, which must be verified after extracting $s'$.
• Figure 4: The hardness of distinguishing between $H_0$ and $H_3$.
• Figure 5: Watermarked images generated using different watermarking methods with the same prompt: "Red dead redemption 2, cinematic view, epic sky, detailed, concept art, low angle, high detail, warm lighting, volumetric, godrays, vivid, beautiful, trending on artstation, by jordan grimmer, huge scene, grass, art greg rutkowski.". Among them, post-processing-based methods (b) (c) (d) and the fine-tuning-based method (e) only add image residuals compared to the original image (a).

Theorems & Definitions (4)

• Definition 1
• Theorem 1
• proof
• Remark 1