Towards synthetic fillers for fair photo lineups: application of generative AI in criminal law proceedings.

A photo lineup is an identification procedure widely used in criminal investigations. It involves presenting a suspect embedded into a set of known-innocent candidates (fillers) to an eyewitness in order to identify the suspect. Compiling fair lineups remains a challenge, particularly when the investigation team has to resort to unsuitable fillers. To support real-world lineups and mitigate the risk of misidentification, we present a practical approach for generating synthetic fillers. By injecting weighted layer-wise noise into a suspects latent vector representation, our approach generates visually distinct fillers while largely preserving demographic characteristics of the suspect. To assess suitability for investigative scenarios, we conducted a large-scale human perception study involving over 450 participants. The results show that the use of synthetically generated fillers leads to balanced identification performance, without making the suspect indistinguishable or stand out. Further experiments show that, compared to previous work, our approach achieves improved preservation of demographic characteristics. Overall, our work contributes to improving fairness in lineups and opens up avenues for supporting criminal investigations through the use of synthetic data.