Perceptual Screen Content Image Hashing Using Adaptive Texture and Shape Features

With the flourishing development of multi-client interactive systems, a new type of digital image known as Screen Content Image (SCI) has emerged. Unlike traditional natural scene images, SCI encompasses various visual contents, including natural images, graphics, and text. Because the multi-region distribution characteristics of screen content images result in the presence of blank regions, malicious modifications are easier to operate and harder to perceive, making a serious threat to visual content security. To this end, this paper proposes a color screen content image hashing algorithm using adaptive text regions features and global shape features. Specifically, the text regions are adaptively collected by calculating the local standard deviation of sub-blocks. Then, quaternion Fourier significant maps are computed for the text regions, and texture statistical features are further extracted to reflect the essential visual content robustness. Moreover, the global shape features are represented from the entire color SCI to ensure the discrimination. Finally, the hash sequence with a length of 142 bits is derived from the above features. Importantly, a specialized tampering dataset for SCIs has been established, and the proposed hashing shows highly sensitive to malicious modifications with a satisfactory detection accuracy. Meanwhile, the ROC curve analysis indicates that the proposed method outperforms existing hashing algorithms.