TMCIH: Perceptual Robust Image Hashing with Transformer-based Multi-layer Constraints

In recent decades, many perceptual image hashing schemes for content authentication have been proposed. However, existing algorithms cannot provide satisfactory robustness and discrimination in the face of complex manipulations in real scenarios. In this work, we propose a novel perceptual robust image hashing scheme with transformer-based multi-layer constraints. Specifically, we first exploit the Transformer structure into the field of perceptual image hashing, and an integrated loss function is designed to optimize the training of the model. In addition, to solve the issue of the simple content-preserving manipulations used in previous datasets, we construct a more challenging image dataset based on various manipulations, which can deal with complex image authentication scenarios. Experimental results demonstrate that our scheme achieves competitive results compared with existing schemes.