On Establishing Edge Adaptive Grid for Bilevel Image Data Hiding

We propose, in this paper, a novel edge-adaptive data hiding method for authenticating binary host images. Through establishing a dense edge-adaptive grid (EAG) along the object contours, we use a simple binary image to show that EAG more efficiently selects good data carrying pixel locations (DCPL) associated with "l-shaped" patterns than block-based methods. Our method employs a dynamic system structure with the redesigned fundamental content adaptive processes (CAP) switch to iteratively trace new contour segments and to search for new DCPLs. By maintaining and updating a location status map, a protective mechanism is proposed to preserve the context of each CAP and their corresponding outcomes. We prove that our method is robust against the interferences caused by close-by contours, image noises, and invariantly selects the same sequence of DCPLs for an arbitrary binary host image and its various marked versions. Comparison shows that our method achieves a good tradeoff between large payload and minimal visual distortion as compared with several classic prior arts for diverse types of binary host images. Moreover, our method well supports state-of-the-art hybrid authentication that integrates data hiding and modern cryptographic techniques.