The Effect of COVID-19 Restrictions on Routine Activities and Online Crime

OBJECTIVES: Routine activity theory suggests that levels of crime are affected by peoples’ activity patterns. Here, we examine if, through their impact on people’s on- and off-line activities, COVID-19 restriction affected fraud committed on- and off-line during the pandemic. Our expectation was that levels of online offending would closely follow changes to mobility and online activity—with crime increasing as restrictions were imposed (and online activity increased) and declining as they were relaxed. For doorstep fraud, which has a different opportunity structure, our expectation was that the reverse would be true. METHOD: COVID-19 restrictions systematically disrupted people’s activity patterns, creating quasi-experimental conditions well-suited to testing the effects of “interventions” on crime. We exploit those conditions using ARIMA time series models and UK data for online shopping fraud, hacking, doorstep fraud, online sales, and mobility to test hypotheses. Doorstep fraud is modelled as a non-equivalent dependent variable, allowing us to test whether findings were selective and in line with theoretical expectations. RESULTS: After controlling for other factors, levels of crime committed online were positively associated with monthly variation in online activities and negatively associated with monthly variation in mobility. In contrast, and as expected, monthly variation in doorstep fraud was positively associated with changes in mobility. CONCLUSIONS: We find evidence consistent with routine activity theory, suggesting that disruptions to people’s daily activity patterns affect levels of crime committed both on- and off-line. The theoretical implications of the findings, and the need to develop a better evidence base about what works to reduce online crime, are discussed

Testing Human Ability To Detect Deepfake Images of Human Faces

Deepfakes are computationally-created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020 a workshop consulting AI experts from academia, policing, government, the private sector, and state security agencies ranked deepfakes as the most serious AI threat. These experts noted that since fake material can propagate through many uncontrolled routes, changes in citizen behaviour may be the only effective defence. This study aims to assess human ability to identify image deepfakes of human faces (StyleGAN2:FFHQ) from nondeepfake images (FFHQ), and to assess the effectiveness of simple interventions intended to improve detection accuracy. Using an online survey, 280 participants were randomly allocated to one of four groups: a control group, and 3 assistance interventions. Each participant was shown a sequence of 20 images randomly selected from a pool of 50 deepfake and 50 real images of human faces. Participants were asked if each image was AI-generated or not, to report their confidence, and to describe the reasoning behind each response. Overall detection accuracy was only just above chance and none of the interventions significantly improved this. Participants' confidence in their answers was high and unrelated to accuracy. Assessing the results on a per-image basis reveals participants consistently found certain images harder to label correctly, but reported similarly high confidence regardless of the image. Thus, although participant accuracy was 62% overall, this accuracy across images ranged quite evenly between 85% and 30%, with an accuracy of below 50% for one in every five images. We interpret the findings as suggesting that there is a need for an urgent call to action to address this threat

Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin

The popularity of online shopping is steadily increasing. At the same time, fake product reviews are published widely and have the potential to affect consumer purchasing behavior. In response, previous work has developed automated methods utilizing natural language processing approaches to detect fake product reviews. However, studies vary considerably in how well they succeed in detecting deceptive reviews, and the reasons for such differences are unclear. A contributing factor may be the multitude of strategies used to collect data, introducing potential confounds which affect detection performance. Two possible confounds are data-origin (i.e., the dataset is composed of more than one source) and product ownership (i.e., reviews written by individuals who own or do not own the reviewed product). In the present study, we investigate the effect of both confounds for fake review detection. Using an experimental design, we manipulate data-origin, product ownership, review polarity, and veracity. Supervised learning analysis suggests that review veracity (60.26-69.87%) is somewhat detectable but reviews additionally confounded with product-ownership (66.19-74.17%), or with data-origin (84.44-86.94%) are easier to classify. Review veracity is most easily classified if confounded with product-ownership and data-origin combined (87.78-88.12%). These findings are moderated by review polarity. Overall, our findings suggest that detection accuracy may have been overestimated in previous studies, provide possible explanations as to why, and indicate how future studies might be designed to provide less biased estimates of detection accuracy

Counterfeits on dark markets: a measurement between Jan-2014 and Sep-2015

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. Estimations in the literature are mostly based on border seizures, but in this paper, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types to those found in seizures but that the share of watches is higher while the share of electronics, clothes, shoes, and Tobacco is lower on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the equivalent products on the surface web appear to be advertised for a multiple of the prices found for darknet markets. We provide some suggestions on how information about darknet market counterfeits could be used by companies and authorities for preventative purposes, showing that insight gathering from the dark web is valuable and could be a cost-effective alternative (or compliment) to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better

Counterfeits on Darknet Markets: A measurement between Jan-2014 and Sep-2015

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. While estimations are mostly based on border seizures, we examined openly labeled counterfeits on darknet markets, which allowed us to gather and analyze information from a different perspective. Here, we analyzed data from 11 darknet markets for the period Jan-2014 and Sep-2015. The findings suggest that darknet markets harbor similar counterfeit product types as found in seizures but that the share of watches is higher and lower for electronics, clothes, shoes, and Tobacco on darknet markets. Also, darknet market counterfeits seem to have similar shipping origins as seized goods, with some exceptions, such as a relatively high share (5%) of dark market counterfeits originating from the US. Lastly, counterfeits on dark markets tend to have a relatively low price and sales volume. However, based on preliminary estimations, the original products on the surface web seem to be worth a multiple of the prices of the counterfeit counterparts on darknet markets. Gathering insights about counterfeits from darknet markets can be valuable for businesses and authorities and be cost-effective compared to border seizures. Thus, monitoring darknet markets can help us understand the counterfeit landscape better.Comment: This paper is a pre-prin

Assessing the Spatial Concentration of Urban Crime: An Insight from Nigeria

Research demonstrates that crime is concentrated. This finding is so consistent that David Weisburd refers to this as the “law of crime concentration at place”. However, most research on crime concentration has been conducted in the US or European cities and has used secondary data sources. In this study, we examine whether the law of crime concentration applies in the context of sub-Saharan Africa using primary data

Understanding and preventing the advertisement and sale of illicit drugs to young people through social media: A multidisciplinary scoping review

ISSUES: The sale of illicit drugs online has expanded to mainstream social media apps. These platforms provide access to a wide audience, especially children and adolescents. Research is in its infancy and scattered due to the multidisciplinary aspects of the phenomena. APPROACH: We present a multidisciplinary systematic scoping review on the advertisement and sale of illicit drugs to young people. Peer-reviewed studies written in English, Spanish and French were searched for the period 2015 to 2022. We extracted data on users, drugs studied, rate of posts, terminology used and study methodology. KEY FINDINGS: A total of 56 peer-reviewed papers were included. The analysis of these highlights the variety of drugs advertised and platforms used to do so. Various methodological designs were considered. Approaches to detecting illicit content were the focus of many studies as algorithms move from detecting drug-related keywords to drug selling behaviour. We found that on average, for the studies reviewed, 13 in 100 social media posts advertise illicit drugs. However, popular platforms used by adolescents are rarely studied. IMPLICATIONS: Promotional content is increasing in sophistication to appeal to young people, shifting towards healthy, glamourous and seemingly legal depictions of drugs. Greater inter-disciplinary collaboration between computational and qualitative approaches are needed to comprehensively study the sale and advertisement of illegal drugs on social media across different platforms. This requires coordinated action from researchers, policy makers and service providers

Preventing repeat victimization: a systematic review

In any given year, most crimes occur against targets that have already been victimized. The crime prevention strategy deriving from this knowledge is that targeting repeat victimization provides a means of allocating crime prevention resources in an efficient and informed manner. This report presents the findings of a systematic review of 31 studies that evaluate efforts to prevent repeat victimization. Most of the evaluations focus on preventing residential burglary, but commercial burglary, domestic violence, and sexual victimization are also covered. The main conclusion is that the evidence shows that repeat victimization can be prevented and crime can be reduced. Over all the evaluations, crimes decreased by one-sixth in the prevention condition compared with the control condition. The decreases were greatest (up to one-fifth) for programmes that were designed to prevent repeat burglaries (residential and commercial). There were fewer evaluations of programmes designed to prevent repeat sexual victimization, but these did not seem to be effective in general. There are indications about what factors increase the success of prevention efforts. Appropriately tailored and implemented situational crime prevention measures, such as target hardening and neighbourhood watch, appear to be the most effective. Advice to victims, and education of victims, are less effective. They are often not prevention measures themselves and do not necessarily lead to the adoption of such measures. The effectiveness of these crime prevention measures increased as the degree of implementation increased. There were many problems of implementation, including poor tailoring of interventions to crime problems, difficulty of recruiting, training and retaining staff, breakdown in communications, data problems, and resistance to tactics by potential recipients or implementers

A comparative analysis to forecast apartment burglaries in Vienna, Austria, based on repeat and near repeat victimization

In this paper, we introduce two methods to forecast apartment burglaries that are based on repeat and near repeat victimization. While the first approach, the “heuristic method” generates buffer areas around each new apartment burglary, the second approach concentrates on forecasting near repeat chain links. These near repeat chain links are events that follow a near repeat pair of an originating and (near) repeat event that is close in space and in time. We name this approach the “near repeat chain method”. This research analyzes apartment burglaries from November 2013 to November 2016 in Vienna, Austria. The overall research goal is to investigate whether the near repeat chain method shows better prediction efficiencies (using a capture rate and the prediction accuracy index) while producing fewer prediction areas. Results show that the near repeat chain method proves to be the more efficient compared to the heuristic method for all bandwidth combinations analyzed in this research.DK W 1237-N23(VLID)286105