COOPERTRIM: Adaptive Data Selection for Uncertainty-Aware Cooperative Perception

Cooperative perception enables autonomous agents to share encoded representations over wireless communication to enhance each other's live situational awareness. However, the tension between the limited communication bandwidth and the rich sensor information hinders its practical deployment. Recent studies have explored selection strategies that share only a subset of features per frame while striving to keep the performance on par. Nevertheless, the bandwidth requirement still stresses current wireless technologies. To fundamentally ease the tension, we take a proactive approach, exploiting the temporal continuity to identify features that capture environment dynamics, while avoiding repetitive and redundant transmission of static information. By incorporating temporal awareness, agents are empowered to dynamically adapt the sharing quantity according to environment complexity. We instantiate this intuition into an adaptive selection framework, COOPERTRIM, which introduces a novel conformal temporal uncertainty metric to gauge feature relevance, and a data-driven mechanism to dynamically determine the sharing quantity. To evaluate COOPERTRIM, we take semantic segmentation and 3D detection as example tasks. Across multiple open-source cooperative segmentation and detection models, COOPERTRIM achieves up to 80.28% and 72.52% bandwidth reduction respectively while maintaining a comparable accuracy. Relative to other selection strategies, COOPERTRIM also improves IoU by as much as 45.54% with up to 72% less bandwidth. Combined with compression strategies, COOPERTRIM can further reduce bandwidth usage to as low as 1.46% without compromising IoU performance. Qualitative results show COOPERTRIM gracefully adapts to environmental dynamics, localization error, and communication latency, demonstrating flexibility and paving the way for real-world deployment.

Source Camera Identification and Detection in Digital Videos through Blind Forensics

Source camera identification in digital videos is the problem of associating an unknown digital video with its source device, within a closed set of possible devices. The existing techniques in source detection of digital videos try to find a fingerprint of the actual source in the video in form of PRNU (Photo Response Non--Uniformity), and match it against the SPN (Sensor Pattern Noise) of each possible device. The highest correlation indicates the correct source. We investigate the problem of identifying a video source through a feature based approach using machine learning. In this paper, we present a blind forensic technique of video source authentication and identification, based on feature extraction, feature selection and subsequent source classification. The main aim is to determine whether a claimed source for a video is actually its original source. If not, we identify its original source. Our experimental results prove the efficiency of the proposed method compared to traditional fingerprint based technique.