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Research on Enhancing the Robustness and Imperceptibility of GAN Watermarking Based on Self-Attention

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2024, 20(4), pp.71-79
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : October 11, 2024
  • Accepted : December 20, 2024
  • Published : December 31, 2024

Jong-Ho Lee 1 Lee, So Young 1 Yongtae Shin 1

1숭실대학교

Accredited

ABSTRACT

Digital image watermarking technology for copyright protection must maintain the visual quality of images while ensuring robust recovery of watermarks under various attack scenarios. This study proposes a GAN-based watermarking model incorporating a Self-Attention mechanism. The proposed model leverages Self-Attention to learn global relationships and critical features within images, simultaneously enhancing the imperceptibility and robustness of watermarked images. Experimental results demonstrate that, under various attack scenarios including JPEG compression, Gaussian noise, blur, cropping, brightness, and contrast adjustments, the Bit Accuracy improved by an average of approximately 7.94%, while the Peak Signal-to-Noise Ratio (PSNR) increased by approximately 1.42dB and the Structural Similarity Index Measure (SSIM) improved by approximately 0.06, showcasing superior performance in both imperceptibility and robustness compared to existing models.

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