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Interpretability of Deep High-Frequency Residuals: A Case Study on SAR Splicing Localization.

Edoardo Daniele Cannas1, Sara Mandelli1, Paolo Bestagini1

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Deep High-Frequency Residuals (DHFRs) enhance multimedia forensics by offering interpretable insights into image manipulation. These deep learning-derived features visually highlight edited areas and reveal tampering techniques in Synthetic Aperture Radar images.

Keywords:
Deep High-Frequency Residuals (DHFRs)Multimedia ForensicsSARexplainabilityimage splicing localizationinterpretabilityxAI

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Area of Science:

  • Computer Science
  • Digital Forensics
  • Artificial Intelligence

Background:

  • Multimedia Forensics (MMF) uses automated techniques to verify content integrity.
  • Neural Networks (NNs) are state-of-the-art in MMF but often lack transparency, limiting critical applications.
  • Deep High-Frequency Residuals (DHFRs) are NN-extracted noise residuals used for image forensics.

Purpose of the Study:

  • To assess the interpretability of Deep High-Frequency Residuals (DHFRs) for multimedia forensics.
  • To determine if DHFRs can reveal the nature of image editing techniques.
  • To explore the potential of DHFRs in image splicing localization.

Main Methods:

  • Investigated DHFRs extracted by NNs from images.
  • Conducted experiments on spliced amplitude Synthetic Aperture Radar (SAR) images.
  • Analyzed the correlation between DHFR appearance and high-frequency energy content in manipulated regions.

Main Results:

  • DHFRs serve as a visual aid for identifying manipulated image regions.
  • DHFRs reveal the specific editing techniques used to tamper with images.
  • A correlation was found between DHFR appearance in tampered zones and their high-frequency energy.

Conclusions:

  • DHFRs possess significant interpretability properties, despite their deep learning origin.
  • DHFRs can enhance image splicing localization and understanding of editing methods.
  • Further research into DHFRs for other forensic applications is encouraged.