Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the\npayload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task\nleft to the steganographer since efficient practical codes exist that embed near the payload-distortion bound. The\npractitioner�s goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this\npaper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be\napplied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of\ncoefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding\nchanges to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy\nregions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as\ntargeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art\nin the spatial domain, JPEG domain, and side-informed JPEG domain.
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