mlsanityData to 7 Oct 2026

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

arXiv 2407.07041 ↗First posted 9 Jul 2024cs.CV cs.AI cs.MMcited by 1

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Abstract (arXiv)

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. Vulnerability is further emphasized by the fact that most SAR products, despite their original complex nature, are often released as amplitude-only information, allowing even inexperienced attackers to edit and easily alter the pixel content. 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 49 references that are in the map. First posted on arXiv 9 Jul 2024.

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11 of 49 references.

References · 11

  1. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  2. 2022TruFor: Leveraging all-round clues for trustworthy image forgery detection and localizationcited by 130
  3. 2018Noiseprint: a CNN-based camera model fingerprintcited by 114
  4. 2020Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networkscited by 28
  5. 2023Diffusion models meet image counter-forensicscited by 6
  6. 2023Realistic Scatterer Based Adversarial Attacks on SAR Image Classifierscited by 5
  7. 2020Adversarial Attack on Deep Learning-Based Splice Localizationcited by 3
  8. 2022An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Imagescited by 3
  9. 2022Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Tracescited by 3
  10. 2022Amplitude SAR Imagery Splicing Localizationcited by 2
  11. 2023Transferable Adversarial Attack on Image Tampering Localizationcited by 2

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