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StyleStegaNet is proposed, a stylized image hiding framework that replaces cover matching with style-concealment transmission, offering a mechanistic explanation for why directly stylized baselines fail and why a reconstruction-guided recovery path is necessary.
This work introduces RemTraceNet, which fuses constrained residuals, local relations, FFT/Haar statistics, and block-DCT evidence at native resolution and outperforms retrained SRNet, ZhuNet, and SiaStegNet baselines by 10.80--15.68 percentage points.
Disentangle and Drop (DnD) is proposed, an attack that is agnostic to the watermark method and treats watermark removal as a representation routing problem and shows that one shared operating setting gives competitive removal with high visual fidelity.
A coarse-to-fine framework that performs watermark removal through wavelet-domain decomposition and residual diffusion refinement, demonstrating that FDDWAN achieves a more favorable trade-off between watermark removal effectiveness and visual fidelity than conventional and learning-based attack methods.
Extensive experiments on representative spatial-domain, transform-domain, orthogonal moment-based, and deep learning-based watermarking schemes demonstrate that SPFM-Net achieves a favorable trade-off between watermark attack effectiveness and perceptual fidelity.
This work identifies two complementary laundering regimes: OpenAI models produce the strongest payload disruption across the evaluated schemes, whereas Nano Banana 2 shows that DwtDct remains vulnerable under high-fidelity reconstruction.
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