Smooth filtering is a common post‐operation which is exploited to blur and conceal the traces of tampered objects. Most of the existing forensic methods aim at detecting only one type of filtering process, such as median filtering or Gaussian filtering, which limits their applications. The authors present a new forensic method based on deep learning technique, which utilises a convolutional neural network (CNN) to automatically learn hierarchical representations from the input images. Unlike conventional CNN models, a modified CNN architecture is specifically designed to identify traces left by the manipulation. A filter layer is added into the CNN. The filtering residual in frequency feature of the input image is extracted by this added layer. The output feature is then fed into the next layer of the CNN. Radon transform is applied to increase the distinctiveness of the residual feature. Experimental results on several public datasets show that the proposed CNN‐based model outperforms some state‐of‐the‐art methods.

As sophisticated photo editing software is increasingly available and the widespread use of multimedia social network, the reliability of digital images becomes more and more important. Photographic splicing, herein defined as a cut-and-paste of image regions from one image onto another image, is difficult to be detected due to the absence of a reference object. To carry out such forensic analysis, we present a novel shadow-based method, with which the fake shadow of the composites can be detected. We show how to estimate the shadow scale factors with a shadow removal technique and, further, how to estimate the growth rate of the penumbra width (GRPW). Inconsistencies in the shadows are then used as evidence of tampering. Compared with other shadow-based forensic methods, the proposed method can not only deal with the problem of shadow cloning in the same image, but also expose the fakery containing the real shadow, which benefit from the estimation of shadow scale factors and GRPW. Comparison results obtained from the splicing forgery detection database verify the ability of our approach.