Verifying image provenance has become an important topic, especially in the realm of news media. To address this issue, the Coalition for Content Provenance and Authenticity (C2PA) developed a standard to verify image provenance that relies on digital signatures produced by cameras. However, photos are usually edited before being published, and a signature on an original photo cannot be verified given only the published edited image. In this work, we describe VerITAS, a system that uses zero-knowledge proofs (zk-SNARKs) to prove that only certain edits have been applied to a signed photo. While past work has created image editing proofs for photos, VerITAS is the first to do so for realistically large images (30 megapixels). Our key innovation enabling this leap is the design of a new proof system that enables proving knowledge of a valid signature on a large amount of witness data. We run experiments on realistically large images that are more than an order of magnitude larger than those tested in prior work. In the case of a computationally weak signer, such as a camera, we are able to generate a proof of valid edits for a 90 MB image in just over thirteen minutes, costing about $0.54 on AWS per image. In the case of a more powerful signer, we are able to generate a proof of valid edits for a 90 MB image in just over three minutes, costing only $0.13 on AWS per image. Either way, proof verification time is less than a second. Our techniques apply broadly whenever there is a need to prove that an efficient transformation was applied correctly to a large amount of signed private data.

This paper proposes large scale collection of pedestrian movement data to promote pedestrian safety in our rapidly developing urban environments. As a first step, we develop and test algorithms for sensing unsafe pedestrian movements. With distracted pedestrian fatalities on the rise, and larger than ever use of smart devices, we propose to use the smartphone to protect pedestrians by leveraging the in-built inertial sensors on the smartphone. We discuss how to use these sensors for recognizing user movements that could be potentially risky when walking on the street, while also accounting for different phone orientations. We introduce a simple path prediction technique and use this to compute potential street crossings. In order to evaluate our algorithms, we conducted walking trials and collected data from all relevant sensors. Initial tests indicate a 90.5% success rate in predicting that a pedestrians trajectory will cross a road.