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- PublicationImproving the accuracy of automated facial age estimation to aid CSEM investigationsThe investigation of violent crimes against individuals, such as the investigation of child sexual exploitation material (CSEM), is one of the more commonly encountered criminal investigation types throughout the world. While hash lists of known CSEM content are commonly used to identify previously encountered material on suspects’ devices, previously unencountered material requires expert, manual analysis and categorisation. The discovery, analysis, and categorisation of these digital images and videos has the potential to be significantly expedited with the use of automated artificial intelligence (AI) based techniques. Intelligent, automated evidence processing and prioritisation has the potential to aid investigators in alleviating some of the digital evidence backlogs that have become commonplace worldwide. In order for AI-aided CSEM investigations to be beneficial, the fundamental question when analysing multimedia content becomes “how old is each subject encountered?’’. Our work presents the evaluation of existing cloud-based and offline age estimation services, introduces our deep learning model, DS13K, which was created with a VGG-16 Deep Convolutional Neural Network (CNN) architecture, and develops an ensemble technique that improves the accuracy of underage facial age estimation. In addition to our model, a number of existing services including Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net, and Deep Expectation (DEX) were used to create an ensemble learning technique. It was found that for the borderline adulthood age range (i.e., 16–17 years old), our DS13K model substantially outperformed existing services, achieving a performance accuracy of 68%. A comparative examination of the obtained results allowed us to identify performance trends and issues inherent to each service/tool and develop ensemble techniques to improve the accuracy of automated adulthood determination.
- PublicationSmarter Password Guessing Techniques Leveraging Contextual Information and OSINTIn recent decades, criminals have increasingly used the web to research, assist and perpetrate criminal behaviour. One of the most important ways in which law enforcement can battle this growing trend is through accessing pertinent information about suspects in a timely manner. A significant hindrance to this is the difficulty of accessing any system a suspect uses that requires authentication via password. Password guessing techniques generally consider common user behaviour while generating their passwords, as well as the password policy in place. Such techniques can offer a modest success rate considering a large/average population. However, they tend to fail when focusing on a single target - especially when the latter is an educated user taking precautions as a savvy criminal would be expected to do. Open Source Intelligence is being increasingly leveraged by Law Enforcement in order to gain useful information about a suspect, but very little is currently being done to integrate this knowledge in an automated way within password cracking. The purpose of this research is to delve into the techniques that enable the gathering of the necessary context about a suspect and find ways to leverage this information within password guessing techniques.
Scopus© Citations 8 17
- PublicationImproving Borderline Adulthood Facial Age Estimation through Ensemble LearningAchieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years. The weakness of the algorithms specifically in the borderline has been a motivation for this paper. In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation (DEX) model. We have achieved an accuracy of 68% for the age group 16 to 17 years old, which is 4 times better than the DEX accuracy for such age range. We also present an evaluation of existing cloud-based and offline facial age prediction services, such as Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net and DEX.
Scopus© Citations 12 277