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This paper benchmarks the robustness of spatial (RivaGAN) and latent (Tree-Ring) watermarks for generative AI against geometric and generative perturbations using an automated attack simulation engine. The study introduces an "Adversarial Evasion Region" (AER) framework to measure cryptographic degradation against semantic visual retention. Results show that spatial watermarks are vulnerable to algorithmic pixel-rewriting while latent watermarks are fragile against geometric misalignment, demonstrating orthogonal vulnerabilities.
Single-domain watermarks are fundamentally insufficient against modern adversarial toolsets, as spatial and latent watermarks exhibit orthogonal vulnerabilities to generative and geometric attacks, respectively.
As open-weights generative AI rapidly proliferates, the ability to synthesize hyper-realistic media has introduced profound challenges to digital trust. Automated disinformation and AI-generated imagery have made robust digital provenance a critical cybersecurity imperative. Currently, state-of-the-art invisible watermarks operate within one of two primary mathematical manifolds: the spatial domain (post-generation pixel embedding) or the latent domain (pre-generation frequency embedding). While existing literature frequently evaluates these models against isolated, classical distortions, there is a critical lack of rigorous, comparative benchmarking against modern generative AI editing tools. In this study, we empirically evaluate two leading representative paradigms, RivaGAN (Spatial) and Tree-Ring (Latent), utilizing an automated Attack Simulation Engine across 30 intensity intervals of geometric and generative perturbations. We formalize an"Adversarial Evasion Region"(AER) framework to measure cryptographic degradation against semantic visual retention (OpenCLIP>75.0). Our statistical analysis ($n=100$ per interval, $MOE = \pm 3.92\%$) reveals that these domains possess mutually exclusive, mathematically orthogonal vulnerabilities. Spatial watermarks experience severe cryptographic degradation under algorithmic pixel-rewriting (exhibiting a 67.47% AER evasion rate under Img2Img translation), whereas latent watermarks exhibit profound fragility against geometric misalignment (yielding a 43.20% AER evasion rate under static cropping). By proving that single-domain watermarking is fundamentally insufficient against modern adversarial toolsets, this research exposes a systemic vulnerability in current digital provenance standards and establishes the foundational exigence for future multi-domain cryptographic architectures.
