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Image Recapture Detection Through Residual-Based Local Descriptors and Machine Learning

机译：通过基于残差的局部描述符和机器学习进行图像捕获检测

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At present, the tamper evidence would be invalid in recaptured image in terms of most of the digital image forensics, so the authenticity of the image detection is a security threat. Since dense local descriptors and machine learning have been successfully applied in steganalysis and forgery detection, we propose a new image recapture detection method based on these two techniques. The local descriptors were recently proposed in the field steganalysis, and some descriptors are selected by greedy strategy in the experiments. Support vector machine and ensemble classifier are utilized as the classifier in the proposed method. The experimental results show that the proposed method achieves a good performance rate that exceeds 99.61% of recaptured images and 96.40% for single captured images on the open source database.
机译：目前，就大多数数字图像取证而言，篡改证据在重新捕获的图像中将是无效的，因此图像检测的真实性是安全威胁。由于密集的局部描述符和机器学习已成功地应用于隐写分析和伪造检测，因此我们基于这两种技术提出了一种新的图像重新捕获检测方法。最近在现场隐写分析中提出了局部描述符，并在实验中通过贪婪策略选择了一些描述符。该方法利用支持向量机和集成分类器作为分类器。实验结果表明，该方法取得了良好的性能，超过了开源数据库中捕获图像的99.61％，单个捕获图像的96.40％。

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