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Although there has been a great deal of work in the use of AI technologies across the law enforcement sector, the legal position of AI and data-driven techniques as part of an evidence chain is currently  indeterminate.  For example, the legal admissibility of automated image and video classification, social network graph analysis, audio participant…

The VALID (Veracity, Agency, Longevity, Integrity and Dignity) project is actively working on frameworks to improve data quality, ethical accountability, and public trust in machine learning projects directed toward law enforcement and community safety outcomes. The first dataset to be constructed under VALID will consist of benign legal images of children.  Images of children in everyday contexts are important assets…

Frameworks for Managing Sensitive Data The challenges of inter-organisational collaborative R&D, are made vastly more difficult if there are constraints on access  due to sensitivities in the data or models themselves. While, a range of techniques have been adopted for collaborative data science involving sensitive data or models, collaboration remains problematic when access to…

Systemic interoperability within and between law enforcement agencies is vital to address the large scale technical challenges inherent in combatting criminal network activity. Historically, law enforcement officers have mediated sense-making between disparate systems as part of investigative workflows.  More recently, however it has become necessary to integrate systems to facilitate modern analytical approaches. Such…

Effective automatic detection of Child Sexual Abuse Material (CSAM) is a continuing challenge, including the rapid detection of previously seen material.  Developing efficient algorithms to identify, retrieve, and classify this material, is a key activity in the technological countering of online child exploitation. Algorithms deployed for this use require robust similarity metrics to match…

A common approach to automatically identifying child sexual abuse material (CSAM), is that of dividing the whole task into several steps which includes face detection, age estimation, and sexual-content analysis. Although numerous researchers have claimed remarkable outputs, the majority of these models are not public or generalized for actual forensic detection.  There is also…

One of the challenges in image processing in a law enforcement context is that of understanding spatio-temporal context. This problem concerns not only inferring the topic of the material, but addressing ‘where and when was this material produced’, ‘is it part of a series’, and/or ‘have we seen material from a similar place or…

Generously funded by the Monash Data Futures Institute, this is a joint research program between the Faculties of Arts, Law and Information Technology. It examines the responses to deepfake technology from the technological, criminological, sociological and legal points of view.  A particular concern of the spread of this technology is misinformation and technology-facilitated abuse.  AiLECS…

Project Metior Telum (“measure the weapon“) is a large scale AiLECS initiative to build a highly accurate automatic firearm detection system. A key part of this project is the construction of a hybrid machine learning dataset, which augments real image data with rendered firearm imagery obtained by 3D scanning and photogrammetry of real-world AFP weaponry holdings.…

Scope The performance of our machine learning tools is directly informed by the quality of training data with which we can work. However,  researchers consistently describe datasets comprising ‘in-the-wild’ images of people as being compiled from content harvested from the open web as well as aggregations of this questionable content. For us, the use…