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

TL;DR

This paper introduces a counter-forensic attack on SAR images that conceals local manipulations by simulating re-acquisition, exploiting SAR data's complex nature to evade detection without needing training or adversarial methods.

Contribution

It presents a novel, generalizable black-box attack method that effectively hides manipulations in SAR images by mimicking re-acquisition, challenging existing forensic detection techniques.

Findings

The attack successfully conceals manipulations across various scenarios.

It is effective without requiring training or adversarial techniques.

The method exploits SAR data's complex nature to evade detection.

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. 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.