Hiding Local Manipulations on SAR Images: A Counter-Forensic Attack

The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. This widespread use and easy availability have unfortunately made SAR data susceptible to malicious alterations, such as local editing applied to the images for inserting or covering the presence of sensitive targets. To contrast malicious manipulations, in the last years the forensic community has begun to dig into the SAR manipulation issue, proposing detectors that effectively localize the tampering traces in amplitude images. Nonetheless, in this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image. We refer to this approach as a counter-forensic attack. To achieve the concealment of manipulation traces, the attacker can simulate a re-acquisition of the manipulated scene by the SAR system that initially generated the pristine image. In doing so, the attacker can obscure any evidence of manipulation, making it appear as if the image was legitimately produced by the system. This attack has unique features that make it both highly generalizable and relatively easy to apply. First, it is a black-box attack, meaning it is not designed to deceive a specific forensic detector. Furthermore, it does not require a training phase and is not based on adversarial operations. We assess the effectiveness of the proposed counter-forensic approach across diverse scenarios, examining various manipulation operations. The obtained results indicate that our devised attack successfully eliminates traces of manipulation, deceiving even the most advanced forensic detectors.

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Publication Details

Journal
IEEE Transactions on Image Processing
Published
2025-01-01
DOI
https://doi.org/10.1109/tip.2025.3643154
Primary Topic
Digital Media Forensic Detection
Type
article
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article

Hiding Local Manipulations on SAR Images: A Counter-Forensic Attack

Stefano Tubaro, Edoardo Daniele Cannas, Stefano Tebaldini, Sara Mandelli et al.
IEEE Transactions on Image Processing
Digital Media Forensic Detection
article

Hiding Local Manipulations on SAR Images: A Counter-Forensic Attack

Stefano Tubaro, Edoardo Daniele Cannas, Stefano Tebaldini, Sara Mandelli, Paolo Bestagini
article en

Abstract

The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. This widespread use and easy availability have unfortunately made SAR data susceptible to malicious alterations, such as local editing applied to the images for inserting or covering the presence of sensitive targets. To contrast malicious manipulations, in the last years the forensic community has begun to dig into the SAR manipulation issue, proposing detectors that effectively localize the tampering traces in amplitude images. Nonetheless, in this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image. We refer to this approach as a counter-forensic attack. To achieve the concealment of manipulation traces, the attacker can simulate a re-acquisition of the manipulated scene by the SAR system that initially generated the pristine image. In doing so, the attacker can obscure any evidence of manipulation, making it appear as if the image was legitimately produced by the system. This attack has unique features that make it both highly generalizable and relatively easy to apply. First, it is a black-box attack, meaning it is not designed to deceive a specific forensic detector. Furthermore, it does not require a training phase and is not based on adversarial operations. We assess the effectiveness of the proposed counter-forensic approach across diverse scenarios, examining various manipulation operations. The obtained results indicate that our devised attack successfully eliminates traces of manipulation, deceiving even the most advanced forensic detectors.

IEEE Transactions on Image ProcessingVol. 34
Politecnico di Milano (IT)
Openalex Percentile: Top 57%
Digital Media Forensic Detection
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Hiding Local Manipulations on SAR Images: A Counter-Forensic Attack — Stefano Tubaro, Edoardo Daniele Cannas, et al. · IEEE Transactions on Image Processing (2025) | TGRS Research Map | TGRS