TIFS Volume 14 Issue 10

Constrained image splicing detection and localization (CISDL), which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other, is a newly proposed challenging task for image forensics. In this paper, we propose a novel adversarial learning framework to learn a deep matching network for CISDL.

Sparse coding-based anomaly detection has shown promising performance, of which the keys are feature learning, sparse representation, and dictionary learning. In this paper, we propose a new neural network for anomaly detection (termed AnomalyNet) by deeply achieving feature learning, sparse representation, and dictionary learning in three joint neural processing blocks. Specifically, to learn better features,...

The importance of normalizing biometric features or matching scores is understood in the multimodal biometric case, but there is less attention to the unimodal case. Prior reports assess the effectiveness of normalization directly on biometric performance. We propose that this process is logically comprised of two independent steps: (1) methods to equalize the effect of each biometric feature on the similarity scores calculated from all the features together...