{'fraction_non_alphanumeric': 0.017793594306049824, 'fraction_numerical': 0.0, 'mean_word_length': 5.492307692307692, '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}}
Adaptive model and neural network based watermark identification

Transform techniques generally are more robust than spatial techniques for watermark embedding. In this research, neural networks and adaptive models are utilized to estimate watermarks in the presence of noise as well as other common image processing attacks in the discrete cosine transform (DCT) and discrete wavelet transform (DWT) domains. The proposed method can be used to semi-blindly determine the estimated watermark. In this paper, a comparative study to a previous method, LMS correlation based detection, is performed and demonstrates the efficacy of the proposed adaptive neural network watermark embedding and detection scheme under different attacks. Finally, the proposed scheme in the DCT transform domain is compared to the proposed scheme in the DWT domain.