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Interpretability of Deep High-Frequency Residuals: A Case Study on SAR Splicing Localization
Edoardo Daniele Cannas1, Sara Mandelli1, Paolo Bestagini1
1Image and Sound Processing Lab (ISPL), Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133 Milan, Italy.
Journal of Imaging
|October 28, 2025
Summary
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.
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.
Keywords:
Deep High-Frequency Residuals (DHFRs)Multimedia ForensicsSARexplainabilityimage splicing localizationinterpretabilityxAIMore Related Videos
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