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. 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.

翻译：合成孔径雷达（SAR）图像通过在线平台的广泛可访问性推动了多个领域的研究。然而，这种广泛使用和易获取性不幸地使SAR数据容易受到恶意篡改，例如对图像进行局部编辑以插入或掩盖敏感目标的存在。尽管SAR产品本质上具有复杂的特性，但大多数仍仅以幅度信息形式发布，这一事实进一步突显了其脆弱性，即使是不熟练的攻击者也能编辑并轻易改变像素内容。为了对抗恶意篡改，取证界近年来开始深入研究SAR篡改问题，提出了能有效定位幅度图像中篡改痕迹的检测器。尽管如此，本文证明，熟练的攻击者可以利用SAR数据的复杂特性，在局部篡改的幅度图像中隐藏所有篡改痕迹。我们将这种方法称为反取证攻击。为实现篡改痕迹的隐藏，攻击者可以模拟SAR系统对篡改场景的重新采集，该系统最初生成了原始图像。通过这种方式，攻击者可以掩盖所有篡改证据，使图像看起来像是系统合法生成的。我们在多种场景下评估了所提出的反取证方法的有效性，并检验了不同的篡改操作。结果表明，我们设计的攻击成功消除了篡改痕迹，甚至能够欺骗最先进的取证检测器。