Scholarlink

<p>With the advent of AI-based image synthesis tools and techniques, Deepfakes have become a serious problem as they pose a massive threat to one’s information security and personal privacy. Several architectures have been proposed to achieve robust Deep Fake detection. However, these methods suffer a drastic drop in performance if the images are visually degraded or have low resolution. To resolve these two issues, a novel <i>FreqFaceNet</i> model has been proposed that employs two novel attentions namely, <i>Wavelet Attention</i> and <i>Fourier Attention</i>, for extracting important frequency-based features from low-resolution images. The extraction of frequency-based features ensures minimal interference of noise due to image compression or low resolution. The proposed model excels on two public benchmark datasets—the DFDC and CelebDF. On the DFDC dataset, FreqFaceNet achieves 98.041% accuracy, an AUC value of 99.748, and a Mathews Correlation Coefficient (MCC) value of 93.857, while on the CelebDF dataset, it obtains an accuracy of 98.325%, an AUC value of 99.81, and an MCC value of 92.819. Qualitative analysis of the proposed model indicates strong classification capabilities. An ablation study has also been conducted to verify the complementary contributions of both Wavelet and Fourier Attention mechanisms.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

FreqFaceNet: an enhanced transformer architecture with dual-order frequency attention for deepfake detection

  • 期刊论文

  • 发表日期：

    2025年2月25日

查看全文
  • Varun Gupta,
  • Vaibhav Srivastava,
  • Ankit Yadav,
  • Dinesh Kumar Vishwakarma,
  • Narendra Kumar

摘要

With the advent of AI-based image synthesis tools and techniques, Deepfakes have become a serious problem as they pose a massive threat to one’s information security and personal privacy. Several architectures have been proposed to achieve robust Deep Fake detection. However, these methods suffer a drastic drop in performance if the images are visually degraded or have low resolution. To resolve these two issues, a novel FreqFaceNet model has been proposed that employs two novel attentions namely, Wavelet Attention and Fourier Attention, for extracting important frequency-based features from low-resolution images. The extraction of frequency-based features ensures minimal interference of noise due to image compression or low resolution. The proposed model excels on two public benchmark datasets—the DFDC and CelebDF. On the DFDC dataset, FreqFaceNet achieves 98.041% accuracy, an AUC value of 99.748, and a Mathews Correlation Coefficient (MCC) value of 93.857, while on the CelebDF dataset, it obtains an accuracy of 98.325%, an AUC value of 99.81, and an MCC value of 92.819. Qualitative analysis of the proposed model indicates strong classification capabilities. An ablation study has also been conducted to verify the complementary contributions of both Wavelet and Fourier Attention mechanisms.