Hopfield network-based approach to detect seam-carved images and identify tampered regions

Seam carving is a content-aware algorithm for image resizing and tampering. This algorithm assigns an energy map to an image and removes the seams with low energy from the image. By doing this, seam carving makes it possible to reduce the image size and eliminate specific content from images. The detection of seam carving has lately been an important but challenging area of research. In past work, we had proposed a method that involved analyzing optimal patch types to recover seams and thus to detect seam-carved images. This method yielded highly accurate detection results. In this paper, we introduce an auto-associated Hopfield network to determine the optimal patch type for seam recovery. We use the Hebbian learning rule to choose, among candidate patch types, the one that most closely resembles the relevant target pattern. Experiments showed that the retrieval process usually converged within eight iterations and that the converged patterns improved the detection accuracy, e.g., with rates of 95.97 and 98.55% for 20 and 50% seam-carved images respectively. We also used this enhanced patch analysis method to identify the seam-carved regions of a tampered image. Its accuracy for the identification of tampered regions was higher than 70% for images with < 30% seam carving.