{'fraction_non_alphanumeric': 0.014925373134328358, 'fraction_numerical': 0.0028429282160625444, 'mean_word_length': 5.257777777777778, '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}}
A Novel Framework design for Semantic based Image retrieval as a Cyber Forensic Tool

Cyber forensics includes the areas of computer forensics, network forensics and internet forensics. Digital images were commonly used in cyber forensics to collect criminal images, fingerprints, images of crime events and so on. Since the current cyber forensic tools are not very much furnished with the course of action of huge image data, it becomes a big issue to obtain image evidence to prosecute the criminal, most of the evidence is available in the form of raw semantics. Cyber forensic investigators often face the challenge of manually examining an enormous quantity of digital image information to identify direct evidence with the assistance of this semantics. Semantic based image retrieval system (SBIR) is therefore the recent and best option to solve this drawback. The main purpose of this research is to design for cyber forensic tools a novel framework of semantic-based image retrieval system. We therefore present a deep learning framework based on the Convolution neural network(GoogLeNet) for recognizing distinct facial expressions from the Yale facial image database for cyber associated forensic tools, the presented framework is very effective for classification and detection based on semantics or verbal descriptions. The network has accomplished a decent accuracy of 86.25 % after training.