{'fraction_non_alphanumeric': 0.0195578231292517, 'fraction_numerical': 0.0, 'mean_word_length': 5.538888888888889, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Turning Cost-Based Steganography into Model-Based

Abstract Most modern steganographic schemes embed secrets by minimizing the total expected cost of modifications. However, costs are usually computed using heuristics and cannot be directly linked to statistical detectability. Moreover, as previously shown by Ker at al., cost-based schemes fundamentally minimize the wrong quantity that makes them more vulnerable to knowledgeable adversary aware of the embedding change rates. In this paper, we research the possibility to convert cost-based schemes to model-based ones by postulating that there exists payload size for which the change rates derived from costs coincide with change rates derived from some (not necessarily known) model. This allows us to find the steganographic Fisher information for each pixel (DCT coefficient), and embed other payload sizes by minimizing deflection. This rather simple measure indeed brings sometimes quite significant improvements in security especially with respect to steganalysis aware of the selection channel. Steganographic algorithms in both spatial and JPEG domains are studied with feature-based classifiers as well as CNNs.