Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection
arXiv:2602.21593v1 Announce Type: new Abstract: Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, however, remains vulnerable to inversion attacks that can recover embedded signals. To mitigate this, recent content-aware semantic watermarking schemes bind watermark signals to high-level image semantics, constraining local edits that would otherwise disrupt global coherence. Yet, large language models (LLMs) possess structured reasoning capabilities that enable targeted exploration of semantic spaces, allowing locally fine-grained but globally coherent semantic alterations that invalidate such bindings. To expose this overlooked vulnerability, we introduce a Coherence-Preserving Semantic Injection (CSI) attack that levera
arXiv:2602.21593v1 Announce Type: new Abstract: Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, however, remains vulnerable to inversion attacks that can recover embedded signals. To mitigate this, recent content-aware semantic watermarking schemes bind watermark signals to high-level image semantics, constraining local edits that would otherwise disrupt global coherence. Yet, large language models (LLMs) possess structured reasoning capabilities that enable targeted exploration of semantic spaces, allowing locally fine-grained but globally coherent semantic alterations that invalidate such bindings. To expose this overlooked vulnerability, we introduce a Coherence-Preserving Semantic Injection (CSI) attack that leverages LLM-guided semantic manipulation under embedding-space similarity constraints. This alignment enforces visual-semantic consistency while selectively perturbing watermark-relevant semantics, ultimately inducing detector misclassification. Extensive empirical results show that CSI consistently outperforms prevailing attack baselines against content-aware semantic watermarking, revealing a fundamental security weakness of current semantic watermark designs when confronted with LLM-driven semantic perturbations.
Executive Summary
The article introduces a novel attack, Coherence-Preserving Semantic Injection (CSI), which exploits the vulnerability of semantic watermarking schemes to large language models (LLMs). CSI leverages LLM-guided semantic manipulation to perturb watermark-relevant semantics, inducing detector misclassification. The attack outperforms existing baselines, revealing a fundamental security weakness in current semantic watermark designs. This has significant implications for the reliability of provenance tracking and forgery prevention in web content.
Key Points
- ▸ Introduction of CSI attack that targets semantic watermarking schemes
- ▸ LLM-guided semantic manipulation enables targeted exploration of semantic spaces
- ▸ CSI attack outperforms existing attack baselines against content-aware semantic watermarking
Merits
Novel Attack Methodology
The article presents a new and effective attack methodology that exploits the vulnerability of semantic watermarking schemes to LLMs.
Demerits
Limited Scope
The article focuses primarily on the vulnerability of semantic watermarking schemes to LLMs, without exploring potential countermeasures or mitigation strategies.
Expert Commentary
The article presents a significant contribution to the field of digital watermarking, highlighting the vulnerability of semantic watermarking schemes to LLMs. The CSI attack demonstrates the potential for targeted exploration of semantic spaces, enabling locally fine-grained but globally coherent semantic alterations that invalidate traditional watermark bindings. This has far-reaching implications for the development of robust and reliable digital watermarking technologies, and underscores the need for ongoing research into countermeasures and mitigation strategies.
Recommendations
- ✓ Further research is needed to develop effective countermeasures against CSI attacks, such as robust watermarking schemes that can detect and respond to LLM-driven semantic perturbations.
- ✓ Policymakers and industry stakeholders should consider the potential implications of LLMs for digital watermarking and content authentication, and develop strategies to mitigate the risks associated with these emerging threats.
