Improving the accuracy of automated facial age estimation to aid CSEM investigations

The 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

Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation

Swift response to the detection of endangered minors is an ongoing concern for law enforcement. Many child-focused investigations hinge on digital evidence discovery and analysis. Automated age estimation techniques are needed to aid in these investigations to expedite this evidence discovery process, and decrease investigator exposure to traumatic material. Automated techniques also show promise in decreasing the overflowing backlog of evidence obtained from increasing numbers of devices and online services. A lack of sufficient training data combined with natural human variance has been long hindering accurate automated age estimation -- especially for underage subjects. This paper presented a comprehensive evaluation of the performance of two cloud age estimation services (Amazon Web Service's Rekognition service and Microsoft Azure's Face API) against a dataset of over 21,800 underage subjects. The objective of this work is to evaluate the influence that certain human biometric factors, facial expressions, and image quality (i.e. blur, noise, exposure and resolution) have on the outcome of automated age estimation services. A thorough evaluation allows us to identify the most influential factors to be overcome in future age estimation systems

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

Digital Forensics AI: on Practicality, Optimality, and Interpretability of Digital Evidence Mining Techniques

Digital forensics as a field has progressed alongside technological advancements over the years, just as digital devices have gotten more robust and sophisticated. However, criminals and attackers have devised means for exploiting the vulnerabilities or sophistication of these devices to carry out malicious activities in unprecedented ways. Their belief is that electronic crimes can be committed without identities being revealed or trails being established. Several applications of artificial intelligence (AI) have demonstrated interesting and promising solutions to seemingly intractable societal challenges. This thesis aims to advance the concept of applying AI techniques in digital forensic investigation. Our approach involves experimenting with a complex case scenario in which suspects corresponded by e-mail and deleted, suspiciously, certain communications, presumably to conceal evidence. The purpose is to demonstrate the efficacy of Artificial Neural Networks (ANN) in learning and detecting communication patterns over time, and then predicting the possibility of missing communication(s) along with potential topics of discussion. To do this, we developed a novel approach and included other existing models. The accuracy of our results is evaluated, and their performance on previously unseen data is measured. Second, we proposed conceptualizing the term “Digital Forensics AI” (DFAI) to formalize the application of AI in digital forensics. The objective is to highlight the instruments that facilitate the best evidential outcomes and presentation mechanisms that are adaptable to the probabilistic output of AI models. Finally, we enhanced our notion in support of the application of AI in digital forensics by recommending methodologies and approaches for bridging trust gaps through the development of interpretable models that facilitate the admissibility of digital evidence in legal proceedings

Image Pre-Processing Algorithm for Age Estimation

Trabajo de Fin de Doble Grado en Ingeniería Informática y Matemáticas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021. El trabajo se puede encontrar en el siguiente enlace: https://gitlab.fdi.ucm.es/miguel.franqueira/ ageestimationHoy en día, el campo de las redes neuronales se encuentra en plena expansión y son muchos los avances que se realizan. Los sistemas de reconocimiento facial han ganado un gran peso en la sociedad en los ´últimos años, incorporándose en el día a día de las personas, por ejemplo, para desbloquear el teléfono móvil. Sin embargo, existen otras muchas tareas por realizar, siendo una de las más importantes el reconocimiento de la edad a través de una imagen. El principal problema encontrado para realizar esta tarea es la poca cantidad de bases de datos dedicadas al tema y la poca calidad de las imágenes en las existentes. En este trabajo se intenta crear diferentes modelos y realizar un pre-procesamiento de las imágenes, para que el entrenamiento de los modelos se realice de manera más precisa. Para cumplir este objetivo, se ha investigado acerca de los conceptos relativos al campo de la estimación de la edad en los trabajos previos relacionados con el tema y en las técnicas más utilizadas y con mejores resultados a la hora de preprocesar imágenes. Finalmente se han probado los conocimientos adquiridos mediante diferentes experimentos para medir la influencia del pre-procesamiento de imágenes en los diferentes modelos creados.Nowadays, neural networks are currently being used to develop many intelligent applications. Facial recognition systems have gained great importance in society in recent years, being incorporated into people’s daily lives, for example, to unlock cell phones. However, there are many other tasks to be solved, one of the most important ones is the age recognition using an image. The more important issue encountered to carry out this task is the small number of databases dedicated to the subject and the poor quality of the images in the existing ones.w Therefore, in this work we try to create different models and implement a pre-processing of the images, so that the training of the models is performed more accurately. To achieve this goal, extensive research has been carried out on the concepts encompassing the field of age estimation, on previous work related to the subject as well as the most widely used and best performing techniques used to preprocess images. Finally, the knowledge acquired has been tested by means of different experiments to measure the influence of image pre-processing on the different models created.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Advancing Human Assessment: The Methodological, Psychological and Policy Contributions of ETS

​This book describes the extensive contributions made toward the advancement of human assessment by scientists from one of the world’s leading research institutions, Educational Testing Service. The book’s four major sections detail research and development in measurement and statistics, education policy analysis and evaluation, scientific psychology, and validity. Many of the developments presented have become de-facto standards in educational and psychological measurement, including in item response theory (IRT), linking and equating, differential item functioning (DIF), and educational surveys like the National Assessment of Educational Progress (NAEP), the Programme of international Student Assessment (PISA), the Progress of International Reading Literacy Study (PIRLS) and the Trends in Mathematics and Science Study (TIMSS). In addition to its comprehensive coverage of contributions to the theory and methodology of educational and psychological measurement and statistics, the book gives significant attention to ETS work in cognitive, personality, developmental, and social psychology, and to education policy analysis and program evaluation. The chapter authors are long-standing experts who provide broad coverage and thoughtful insights that build upon decades of experience in research and best practices for measurement, evaluation, scientific psychology, and education policy analysis. Opening with a chapter on the genesis of ETS and closing with a synthesis of the enormously diverse set of contributions made over its 70-year history, the book is a useful resource for all interested in the improvement of human assessment