Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.

中文翻译：

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混合深度学习和机器学习方法用于被动图像取证

使用传统算法进行图像伪造检测需要花费大量时间才能找到伪造品。用于检测图像伪造的新新兴方法使用深度神经网络算法。这项研究使用基于混合深度学习（DL）和机器学习的方法进行被动图像伪造检测。DL算法将图像分为伪造和非伪造类别，而彩色照明则定位伪造。将模拟结果与公共数据集上的其他算法进行比较。仿真结果表明，CASIA1.0的准确性为99％，CASIA2.0的准确性为98％，BSDS300的准确性为98％，DVMM的准确性为97％，CMFD图像处理数据集的准确性为99％。