신경망 기반 스타일 변환을 방해할 수 있는 영역별 노이즈 생성 기법 = Region-Specific Noise Generation for Untransferable Examples Against Neural Style Transfer

다국어 초록 (Multilingual Abstract)

Neural Style Transfer poses a significant threat to the intellectual property rights of digital artists, as it can extract and replicate unique artistic styles without consent or compensation. Existing protection techniques face a fundamental trade-off: they either degrade image quality for effective protection or offer minimal protection to preserve quality. This paper proposes a novel region-specific noise generation that solves this trade-off by spatially segregating protection objectives. Our method divides an image into two distinct regions and applies different perturbation strategies to each: a small patch region (2% of image area) receives strong perturbations to hijack the attention mechanism, while the background region (98%) is subjected to minimal perturbations to corrupt global feature statistics. The key innovation lies in the spatial separation that fundamentally eliminates gradient interference between different loss objectives.
Experimental results demonstrate that our method achieves over 90% reduction in style transfer effectiveness (STDR 0.8995) while maintaining high visual quality (SSIM 0.8624), representing a 38% improvement in protection efficiency compared to existing methods.