Detection of content-aware image resizing based on Benford's law

Content-aware image resizing is currently widely used because it maintains the original appearance of important objects to the greatest extent when the aspect ratio of an image changes during resizing. Content-aware image resizing techniques, such as seam carving, are also used for image forgery. A new Benford's law-based algorithm for detecting content-aware resized images is presented. The algorithm extracts features on the basis of the first digit distribution of the discrete cosine transform coefficients, which follow the standard Benford's law. We trained these features from both normal images and content-aware resized images using a support vector machine. The experimental results show that the proposed method can efficiently distinguish a content-aware resized image from a normal image, and its precision is better than that of existing methods, including those based on Markov features and others.