AI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the
  Era of Artificial Illusions
Mirko Casu Luca Guarnera Pasquale Caponnetto Sebastiano Battiato
This paper provides a comprehensive analysis of cognitive biases in forensics
and digital forensics, examining their implications for decision-making
processes in these fields. It explores the various types of cognitive biases
that may arise during forensic investigations and digital forensic analyses,
such as confirmation bias, expectation bias, overconfidence in errors,
contextual bias, and attributional biases. It also evaluates existing methods
and techniques used to mitigate cognitive biases in these contexts, assessing
the effectiveness of interventions aimed at reducing biases and improving
decision-making outcomes. Additionally, this paper introduces a new cognitive
bias, called "impostor bias", that may affect the use of generative Artificial
Intelligence (AI) tools in forensics and digital forensics. The impostor bias
is the tendency to doubt the authenticity or validity of the output generated
by AI tools, such as deepfakes, in the form of audio, images, and videos. This
bias may lead to erroneous judgments or false accusations, undermining the
reliability and credibility of forensic evidence. The paper discusses the
potential causes and consequences of the impostor bias, and suggests some
strategies to prevent or counteract it. By addressing these topics, this paper
seeks to offer valuable insights into understanding cognitive biases in
forensic practices and provide recommendations for future research and
practical applications to enhance the objectivity and validity of forensic
investigations.