CVOct 8, 2025

SpecGuard: Spectral Projection-based Advanced Invisible Watermarking

arXiv:2510.07302v12 citationsh-index: 5Has Code
Originality Incremental advance
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This addresses the challenge of protecting copyrighted images against distortions and adversarial perturbations, representing an incremental improvement over existing watermarking methods.

The paper tackles the problem of robust and invisible image watermarking by introducing SpecGuard, which embeds messages in hidden convolution layers using spectral projection and wavelet decomposition, achieving state-of-the-art performance in invisibility, capacity, and robustness against various attacks.

Watermarking embeds imperceptible patterns into images for authenticity verification. However, existing methods often lack robustness against various transformations primarily including distortions, image regeneration, and adversarial perturbation, creating real-world challenges. In this work, we introduce SpecGuard, a novel watermarking approach for robust and invisible image watermarking. Unlike prior approaches, we embed the message inside hidden convolution layers by converting from the spatial domain to the frequency domain using spectral projection of a higher frequency band that is decomposed by wavelet projection. Spectral projection employs Fast Fourier Transform approximation to transform spatial data into the frequency domain efficiently. In the encoding phase, a strength factor enhances resilience against diverse attacks, including adversarial, geometric, and regeneration-based distortions, ensuring the preservation of copyrighted information. Meanwhile, the decoder leverages Parseval's theorem to effectively learn and extract the watermark pattern, enabling accurate retrieval under challenging transformations. We evaluate the proposed SpecGuard based on the embedded watermark's invisibility, capacity, and robustness. Comprehensive experiments demonstrate the proposed SpecGuard outperforms the state-of-the-art models. To ensure reproducibility, the full code is released on \href{https://github.com/inzamamulDU/SpecGuard_ICCV_2025}{\textcolor{blue}{\textbf{GitHub}}}.

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