Reports regarding the misuse of $\textit{Generative AI}$ ($\textit{GenAI}$)
to create harmful deepfakes are emerging daily. Recently, defensive
watermarking, which enables $\textit{GenAI}$ providers to hide fingerprints in
their images to later use for deepfake detection, has been on the rise. Yet,
its potential has not been fully explored. We present $\textit{UnMarker}$ --
the first practical $\textit{universal}$ attack on defensive watermarking.
Unlike existing attacks, $\textit{UnMarker}$ requires no detector feedback, no
unrealistic knowledge of the scheme or similar models, and no advanced
denoising pipelines that may not be available. Instead, being the product of an
in-depth analysis of the watermarking paradigm revealing that robust schemes
must construct their watermarks in the spectral amplitudes, $\textit{UnMarker}$
employs two novel adversarial optimizations to disrupt the spectra of
watermarked images, erasing the watermarks. Evaluations against the
$\textit{SOTA}$ prove its effectiveness, not only defeating traditional schemes
while retaining superior quality compared to existing attacks but also breaking
$\textit{semantic}$ watermarks that alter the image's structure, reducing the
best detection rate to $43\%$ and rendering them useless. To our knowledge,
$\textit{UnMarker}$ is the first practical attack on $\textit{semantic}$
watermarks, which have been deemed the future of robust watermarking.
$\textit{UnMarker}$ casts doubts on the very penitential of this countermeasure
and exposes its paradoxical nature as designing schemes for robustness
inevitably compromises other robustness aspects.

通过对水印范式进行深入分析，我们提出了第一个实际的对抗语义性水印的攻击 UnMarker，该攻击不仅可以击败传统方案并且保持更高的质量，还可以破坏修改图像结构的语义性水印，将最佳检测率降低到 43％并使其无用。