Adaptive information hiding based on local sparsity

This paper presents a novel adaptive steganographic scheme in sparse domain. Image blocks used to hide secret information are selected by sparsity of their sparse decomposition coefficients. High sparsity means less nonzero entries in the coefficient vector. We sort image blocks in ascending order according to their sparsity. Low sparsity blocks have higher priority to convey secret information. We prove that blocks with more nonzero elements in their decomposition coefficients are more complex. Consequently, image blocks of high local complexity are utilized. Besides, the embedding rule in sparse domain will not change the sparsity of decomposition coefficients of blocks, which guarantees the block consistency in the extraction process. Experimental results demonstrate that our algorithm produces stego images with higher visual quality and has far better anti-detection performance than other sparse domain methods.