{'fraction_non_alphanumeric': 0.014349775784753363, 'fraction_numerical': 0.0, 'mean_word_length': 5.4508670520231215, '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}}
Identifying camera and processing from cropped JPEG photos via tensor analysis

Digital image and video forensics is to detect the authenticity of digital images/videos. So far, existing algorithms are designed by exploring one or several special features, which usually result in limited performance and applicability. With the perspective of entire procedure including image acquisition and image processing, a theory of general blind image forensics is proposed in this paper. Then a new blind image forensic method is designed based on this theory to identify camera source and processing history of cropped compressed digital images. In this method, tensor decomposition analysis is applied to extract features of nonlinear operations, which come from both algorithms embedded within camera and operations done by post-software. Then, the Support Vector Machine is utilized to classify whether the target image is captured by the claimed camera and underwent the declared processing history. Experimental results show that this method has high detection accuracy, which demonstrated that our theory is correct.