Error Resilient Image Authentication Using Feature Statistical and Spatial Properties

Abstract

The pervasive distribution of digital images triggers an emergent need of authenticating degraded images by lossy compression and transmission. This paper proposes a robust content-based image authentication scheme for image transmissions over lossy channels. Content-based image authentication typically assesses authenticity based on a distance measure of feature differences between the testing image and its original. Commonly employed distance measures such as the Minkowski measures may not be adequate for content-based image authentication since they do not exploit statistical and spatial properties of the feature differences. This proposed error resilient scheme is based on a statistics- and spatiality-based measure (SSM) of feature differences. This measure is motivated by an observation that most malicious manipulations are localized whereas acceptable manipulations cause global distortions. Experimental results show that SSM is better than previous used measures in distinguishing malicious manipulations from acceptable ones, and the proposed SSM-based scheme is robust to transmission errors and other acceptable manipulations, and is sensitive malicious image modifications.