Recent advances in machine learning and computer vision have made it simple to manipulate a variety of media, including satellite images. Most of the commercially available satellite images go through the process of orthorectification to remove potential distortions due to terrain variations. This orthorectification process typically involves the use of rational polynomial coefficients (RPC) that geometrically remap the pixels in the original image to the rectified image. This paper proposes the first method to verify the authenticity of RPC metadata in an orthorectified satellite image. The steps include calculating the Residual Discrete Fourier Transform (DFT) pattern from the image using a linear predictor based residual spectral analysis and comparing with Expected Residual pattern that is obtained using the RPC metadata associated with the image. If the metadata associated with orthorectified image is correct, then the Residual-DFT pattern (which represents image data) and the Expected-Residual-DFT pattern (which represents metadata) should be similar. We use SSIM (Structural Similarity Index Metric) to quantify the similarity and thereby verify if the data has been tampered or not. Detailed experimental results demonstrate that our method achieves over 97% accuracy in the majority of binary tampering detection tests.

中文翻译：

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卫星图像中基于重采样估计的 RPC 元数据验证


机器学习和计算机视觉的最新进展使得操纵各种媒体（包括卫星图像）变得简单。大多数商业卫星图像都会经过正射校正过程，以消除由于地形变化而造成的潜在失真。此正射校正过程通常涉及使用有理多项式系数 (RPC)，将原始图像中的像素几何重新映射到校正图像。本文提出了第一种验证正射卫星图像中 RPC 元数据真实性的方法。这些步骤包括使用基于线性预测器的残差谱分析计算图像的残差离散傅立叶变换 (DFT) 图案，并与使用与图像关联的 RPC 元数据获得的预期残差图案进行比较。如果与正射校正图像关联的元数据正确，则残差 DFT 模式（表示图像数据）和预期残差 DFT 模式（表示元数据）应该相似。我们使用SSIM（结构相似度指数度量）来量化相似度，从而验证数据是否被篡改。详细的实验结果表明，我们的方法在大多数二进制篡改检测测试中都能达到 97% 以上的准确率。