Content fingerprinting recently emerges as an effective nonintrusive solution for copyright protection. Fingerprinting algorithm maps the perceptual contents of media file to an invariant digest, so that unauthorized copies can be identified via fingerprint comparison. This letter presents a distortion-resistant sparse coding strategy for image fingerprinting that simulates the hierarchical information processing flow of visual system. Sparse coding, which seeks a small set of atoms that can best represent input signal, helps fingerprinting algorithm detect the intrinsic visual features of image. However, the high freedom of atom selection makes sparse coding sensitive to distortion. In this letter, several measures are applied on sparse coding and dictionary learning to jointly ensure the invariance of fingerprint, such as imposing the neighborhood-priority principle on atom selection, regulating the layout of atoms, and forcing sparse codes to preserve the distance in the image space. Content identification performance of the proposed work was tested on a database of 219 000 images. The error rate of the proposed algorithm is at least ten times lower than state-of-the-arts, and satisfactory performance was observed even under extremely low bit budget.