Advances in Secure Data Processing and Anomaly Detection

The field of secure data processing and anomaly detection is witnessing significant developments, with a focus on innovative encryption methods, adaptive security architectures, and effective anomaly detection techniques. Researchers are exploring new approaches to secure data transmission and storage, including the use of facial images and passwords for key generation, as well as distributed broadcast encryption with linear-size public parameters. Additionally, zero-shot anomaly detection is gaining attention, with proposed methods leveraging visual-perception prompting and attention head adaptation to improve detection accuracy. Another crucial aspect is the security of machine learning models, with the identification of supply chain risks and potential solutions to bring transparency to open ML models. Noteworthy papers include:

  • ViP$^2$-CLIP, which introduces a visual-perception prompting mechanism for zero-shot anomaly detection, achieving state-of-the-art performance on industrial and medical benchmarks.
  • HeadCLIP, which effectively adapts attention heads for zero-shot anomaly detection, demonstrating improvements in pixel and image-level anomaly detection scores.
  • A study on the supply chain risks of open ML models, proposing the use of Sigstore to bring transparency to model publishers and prove properties about the datasets used.

Sources

Dynamic Encryption-Based Cloud Security Model using Facial Image and Password-based Key Generation for Multimedia Data

Adaptively Secure Distributed Broadcast Encryption with Linear-Size Public Parameters

ViP$^2$-CLIP: Visual-Perception Prompting with Unified Alignment for Zero-Shot Anomaly Detection

Domain Adaptation of Attention Heads for Zero-shot Anomaly Detection

Machine Learning Models Have a Supply Chain Problem

Joint Data Hiding and Partial Encryption of Compressive Sensed Streams

Built with on top of