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In this paper, I propose and explore a method for image location classification. Most existing works concentrate on outdoor scenes as scenery or an iconic landmark make it easier to point out the location. Few researchers have addressed the issue of indoor scenes. Although indoor images increase the difficulty of tracking geolocation, it is necessary to respond to this shortcoming as many crimes happen indoors.

To address this problem, I propose a method for indoor image location classification by segmenting patterns of extracted objects from images. Specifically, I extract objects from images. Then, based on the accuracy levels of the bounding boxes of specific kinds of objects in the image, I only crop that kind of objects from original images. Moreover, I segment patterns from the extracted objects and crop those patterns by thresholding techniques. To classify images by these segmented patterns, I employ convolutional neural networks. Experimental results in the dataset of hotel rooms across the globe show promising accuracies, which witnesses that my method contributes to ultimately identifying the hotel chain which the image belongs to from the hotel dataset.

Wilson, C., Dalins, J. & Rolan, G., 2020, 2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 186-191 6 p. 9311021

We describe the Artificial Intelligence for Law Enforcement and Community Safety (AiLECS) research laboratory, a collaboration between the Australian Federal Police and Monash University. The laboratory was initially motivated by work towards countering online child exploitation material. It now offers a platform for further research and development in AI that will benefit policing and mitigating threats to community wellbeing more broadly. We outline the work the laboratory has undertaken, results to date, and discuss our agenda for scaling up its work into the future.

Dalins, Janis, Campbell Wilson, and Mark Carman. “Criminal motivation on the dark web: A categorisation model for law enforcement.” Digital Investigation 24 (2018): 62-71.

Research into the nature and structure of ‘Dark Webs’ such as Tor has largely focused upon manually labelling a series of crawled sites against a series of categories, sometimes using these labels as a training corpus for subsequent automated crawls. Such an approach is adequate for establishing broad taxonomies, but is of limited value for specialised tasks within the field of law enforcement. Contrastingly, existing research into illicit behaviour online has tended to focus upon particular crime types such as terrorism. A gap exists between taxonomies capable of holistic representation and those capable of detailing criminal behaviour. The absence of such a taxonomy limits interoperability between agencies, curtailing development of standardised classification tools.

We introduce the Tor-use Motivation Model (TMM), a two-dimensional classification methodology specifically designed for use within a law enforcement context. The TMM achieves greater levels of granularity by explicitly distinguishing site content from motivation, providing a richer labelling schema without introducing inefficient complexity or reliance upon overly broad categories of relevance. We demonstrate this flexibility and robustness through direct examples, showing the TMM’s ability to distinguish a range of unethical and illegal behaviour without bloating the model with unnecessary detail.

The authors of this paper received permission from the Australian government to conduct an unrestricted crawl of Tor for research purposes, including the gathering and analysis of illegal materials such as child pornography. The crawl gathered 232,792 pages from 7651 Tor virtual domains, resulting in the collation of a wide spectrum of materials, from illicit to downright banal. Existing conceptual models and their labelling schemas were tested against a small sample of gathered data, and were observed to be either overly prescriptive or vague for law enforcement purposes – particularly when used for prioritising sites of interest for further investigation.

In this paper we deploy the TMM by manually labelling a corpus of over 4000 unique Tor pages. We found a network impacted (but not dominated) by illicit commerce and money laundering, but almost completely devoid of violence and extremism. In short, criminality on this ‘dark web’ is based more upon greed and desire, rather than any particular political motivations.