{'fraction_non_alphanumeric': 0.024096385542168676, 'fraction_numerical': 0.0, 'mean_word_length': 5.921875, '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}}
Tackling the Cover Source Mismatch Problem in Audio Steganalysis With Unsupervised Domain Adaptation

Nowadays, the convolutional neural network (CNN) based steganalysis has achieved remarkable performance in the well-controlled lab environment. However, the cover source mismatch (CSM) problem, which can be attributed to the discrepancy between the training, and evaluation datasets, is still one of the pivotal obstacles for adapting the steganalysis into real-world applications. In this letter, we propose to merge the domain adaptation strategy into CNN-based audio steganalysis for handling the CSM problem. Specifically, the proposed framework contains three components: feature extractor, steganalytic classifier, and domain discriminator. The cascade of feature extractor, and steganalytic classifier compose the typical supervised steganalysis model. The unsupervised domain adaptation is implemented by the domain adversarial training between the feature extractor, and domain discriminator. Ultimately, the feature extractor is trained to extract the steganalytic, and domain-invariant features. It aims to reduce the domain gap between the training data, and testing data. The experimental results show that our approach could effectively mitigate the CSM impact caused by the diversity of audio recording devices.