{'fraction_non_alphanumeric': 0.01978021978021978, 'fraction_numerical': 0.006593406593406593, 'mean_word_length': 5.326388888888889, '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}}
SVD-based image splicing detection

Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most common image forgery techniques. To detect the spliced images several methods proposed utilizing the statistical features of the digital images. In this paper, a new image splicing detection approach proposed based on singular value decomposition (SVD) feature extraction method applied in steganalysis. SVD-based features are merged with discrete cosine transform (DCT) for image splicing detection. Support vector machine is used to distinguish between authentic and spliced images. The results show a detection accuracy of 78.82% is achieved for the proposed method with only 50 dimensional feature vector. Furthermore the performance of SVD-based features needs more improvement in image splicing detection area of work.