Naturalistic driving studies consist of drivers using their personal vehicles and provide valuable real-world data, but privacy issues must be handled very carefully. Drivers sign a consent form when they elect to participate, but passengers do not for a variety of practical reasons. However, their privacy must still be protected. One large study includes a blurred image of the entire cabin which allows reviewers to find passengers in the vehicle; this protects the privacy but still allows a means of answering questions regarding the impact of passengers on driver behavior. A method for automatically counting the passengers would have scientific value for transportation researchers. We investigated different image analysis methods for automatically locating and counting the non-drivers including simple face detection and fine-tuned methods for image classification and a published object detection method. We also compared the image classification using convolutional neural network and vision transformer backbones. Our studies show the image classification method appears to work the best in terms of absolute performance, although we note the closed nature of our dataset and nature of the imagery makes the application somewhat niche and object detection methods also have advantages. We perform some analysis to support our conclusion.
There are advantages and disadvantages to both robust and cryptographic hash methods. Integrating the qualities of robustness and cryptographic confidentiality would be highly desirable. However, the challenge is that the concept of similarity is not applicable to cryptographic hashes, preventing direct comparison between robust and cryptographic hashes. Therefore, when incorporating robust hashes into cryptographic hashes, it becomes essential to develop methods that effectively capture the intrinsic properties of robust hashes without compromising their robustness. In order to accomplish this, it is necessary to anticipate the hash bits that are most susceptible to modification, such as those that are affected by JPEG compression. Our work demonstrates that the prediction accuracy of existing approaches can be significantly improved by using a new hybrid hash comparison strategy.
Video conferencing usage dramatically increased during the pandemic and is expected to remain high in hybrid work. One of the key aspects of video experience is background blur or background replacement, which relies on good quality portrait segmentation in each frame. Software and hardware manufacturers have worked together to utilize depth sensor to improve the process. Existing solutions have incorporated depth map into post processing to generate a more natural blurring effect. In this paper, we propose to collect background features with the help of depth map to improve the segmentation result from the RGB image. Our results show significant improvements over methods using RGB based networks and runs faster than model-based background feature collection models.
Both robust and cryptographic hash methods have advantages and disadvantages. It would be ideal if robustness and cryptographic confidentiality could be combined. The problem here is that the concept of similarity of robust hashes cannot be applied to cryptographic hashes. Therefore, methods must be developed to reliably intercept the degrees of freedom of robust hashes before they are included in a cryptographic hash, but without losing their robustness. To achieve this, we need to predict the bits of a hash that are most likely to be modified, for example after a JPEG compression. We show that machine learning can be used to make a much more reliable prediction than the approaches previously discussed in the literature.
We present a scheme for securely communicating data over close distances in public settings. Exploiting the ubiquity of cameras in modern day devices and the high resolution of displays, our approach provides secure data communication over short distances by using specialized 2-D barcodes along with an adaptive protocol. Specifically, the barcodes carry public data compatible with their conventional design and additionally private data through specialized orientation modulation in the barcode modules. The latter is reliably decoded when the barcodes are captured at close distances but not from farther distances, a property that we call “proximal privacy”. The adaptive protocol dynamically modifies the strength of the orientation modulation until it is just recoverable by the capture camera. We validate our approach via simulations and by using physical devices to display and capture the specialized barcodes.