Visible Policing: Technology, Transparency, and Democratic Control

Law enforcement has an opacity problem. Police use sophisticated technologies to monitor individuals, surveil communities, and predict behaviors in increasingly intrusive ways. But legal institutions have struggled to understand—let alone set limits on—new investigative methods and techniques for two major reasons. First, new surveillance technology tends to operate in opaque and unaccountable ways, augmenting police power while remaining free of meaningful oversight. Second, shifts in Fourth Amendment doctrine have expanded law enforcement’s ability to engage in surveillance relatively free of scrutiny by courts or by the public. The result is that modern policing is not highly visible to oversight institutions or the public and is becoming even less so. In light of these informational dynamics, transparency litigation has become a core technique for rendering obscure investigative practices visible and holding police accountable. These new lawsuits form a criminal procedure “shadow docket”—they resolve important questions about democratic governance of policing without deciding on the constitutionality of searches and seizures. This Article builds on the government secrecy literature to explore the significance of this “shadow docket” and the relationship between transparency obligations and constitutional limits on police action. In the absence of meaningful Fourth Amendment safeguards, transparency litigation makes policing practices increasingly visible to the public and democratic institutions in areas where constitutional criminal procedure today has minimal reach. These efforts to make policing visible bear important lessons for advocates and scholars of criminal procedure, criminal justice reform, and transparency itself

License Plate Recognition using Convolutional Neural Networks Trained on Synthetic Images

In this thesis, we propose a license plate recognition system and study the feasibility of using synthetic training samples to train convolutional neural networks for a practical application. First we develop a modular framework for synthetic license plate generation; to generate different license plate types (or other objects) only the first module needs to be adapted. The other modules apply variations to the training samples such as background, occlusions, camera perspective projection, object noise and camera acquisition noise, with the aim to achieve enough variation of the object that the trained networks will also recognize real objects of the same class. Then we design two convolutional neural networks of low-complexity for license plate detection and character recognition. Both are designed for simultaneous classification and localization by branching the networks into a classification and a regression branch and are trained end-to-end simultaneously over both branches, on only our synthetic training samples. To recognize real license plates, we design a pipeline for scale invariant license plate detection with a scale pyramid and a fully convolutional application of the license plate detection network in order to detect any number of license plates and of any scale in an image. Before character classification is applied, potential plate regions are un-skewed based on the detected plate location in order to achieve an as optimal representation of the characters as possible. The character classification is also performed with a fully convolutional sweep to simultaneously find all characters at once. Both the plate and the character stages apply a refinement classification where initial classifications are first centered and rescaled. We show that this simple, yet effective trick greatly improves the accuracy of our classifications, and at a small increase of complexity. To our knowledge, this trick has not been exploited before. To show the effectiveness of our system we first apply it on a dataset of photos of Italian license plates to evaluate the different stages of our system and which effect the classification thresholds have on the accuracy. We also find robust training parameters and thresholds that are reliable for classification without any need for calibration on a validation set of real annotated samples (which may not always be available) and achieve a balanced precision and recall on the set of Italian license plates, both in excess of 98%. Finally, to show that our system generalizes to new plate types, we compare our system to two reference system on a dataset of Taiwanese license plates. For this, we only modify the first module of the synthetic plate generation algorithm to produce Taiwanese license plates and adjust parameters regarding plate dimensions, then we train our networks and apply the classification pipeline, using the robust parameters, on the Taiwanese reference dataset. We achieve state-of-the-art performance on plate detection (99.86% precision and 99.1% recall), single character detection (99.6%) and full license reading (98.7%)

ViewMap: Sharing Private In-Vehicle Dashcam Videos

Today, search for dashcam video evidences is conducted manually and its procedure does not guarantee privacy. In this paper, we motivate, design, and implement ViewMap, an automated public service system that enables sharing of private dashcam videos under anonymity. ViewMap takes a profile-based approach where each video is represented in a compact form called a view profile (VP), and the anonymized VPs are treated as entities for search, verification, and reward instead of their owners. ViewMap exploits the line-of-sight (LOS) properties of dedicated short-range communications (DSRC) such that each vehicle makes VP links with nearby ones that share the same sight while driving. ViewMap uses such LOS-based VP links to build a map of visibility around a given incident, and identifies VPs whose videos are worth reviewing. Original videos are never transmitted unless they are verified to be taken near the incident and anonymously solicited. ViewMap offers untraceable rewards for the provision of videos whose owners remain anonymous. We demonstrate the feasibility of ViewMap via field experiments on real roads using our DSRC testbeds and trace-driven simulations.We sincerely thank our shepherd Dr. Ranveer Chandra and the anonymous reviewers for their valuable feedback. This work was supported by Samsung Research Funding Center for Future Technology under Project Number SRFC-IT1402-01

Spartan Daily, April 7, 1987

Volume 88, Issue 49https://scholarworks.sjsu.edu/spartandaily/7574/thumbnail.jp

Currency design in the United States and abroad: counterfeit deterrence and visual accessibility

Despite the increasing use of electronic payments, currency retains an important role in the payment system of every country. In this article, the authors compare and contrast trade-offs among currency design features, including those primarily intended to deter counterfeiting and those to improve usability by the visually impaired. The authors conclude that periodic changes in the design of currency are an important aspect of counterfeit deterrence and that currency designers worldwide generally have been successful in efforts to deter counterfeiting. At the same time, currency designers have sought to be sensitive to the needs of the visually impaired. Although trade-offs among goals sometimes have forced compromises, new technologies promise banknotes that are both more difficult to counterfeit and more accessible to the visually impaired. Among the world's currencies, U.S. banknotes are the notes most widely used outside their country of issue and thus require special consideration.Paper money design - United States ; Money