Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act

Lorch B, Scheler N, Riess C (2022)


Publication Language: English

Publication Type: Conference contribution

Publication year: 2022

Event location: Belgrade RS

URI: https://faui1-files.cs.fau.de/public/publications/mmsec/2022-Lorch-AIA-EUSIPCO.pdf

DOI: 10.23919/eusipco55093.2022.9909723

Abstract

In many applications of forensic image analysis, state-of-the-art results are nowadays achieved with machine learning methods. However, concerns about their reliability and opaqueness raise the question whether such methods can be used in criminal investigations. So far, this question of legal compliance has hardly been discussed, also because legal regulations for machine learning methods were not defined explicitly. To this end, the European Commission recently proposed the artificial intelligence (AI) act, a regulatory framework for the trustworthy use of AI. Under the draft AI act, high-risk AI systems for use in law enforcement are permitted but subject to compliance with mandatory requirements. In this paper, we review why the use of machine learning in forensic image analysis is classified as high-risk. We then summarize the mandatory requirements for high-risk AI systems and discuss these requirements in light of two forensic applications, license plate recognition and deep fake detection. The goal of this paper is to raise awareness of the upcoming legal requirements and to point out avenues for future research.

Authors with CRIS profile

Related research project(s)

How to cite

APA:

Lorch, B., Scheler, N., & Riess, C. (2022). Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act. In Proceedings of the European Signal Processing Conference. Belgrade, RS.

MLA:

Lorch, Benedikt, Nicole Scheler, and Christian Riess. "Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act." Proceedings of the European Signal Processing Conference, Belgrade 2022.

BibTeX: Download