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This paper provides a comprehensive analysis of cognitive biases in forensicsand digital forensics, examining their implications for decision-makingprocesses in these fields. It explores the various types of cognitive biasesthat 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 methodsand techniques used to mitigate cognitive biases in these contexts, assessingthe effectiveness of interventions aimed at reducing biases and improvingdecision-making outcomes. Additionally, this paper introduces a new cognitivebias, called "impostor bias", that may affect the use of generative ArtificialIntelligence (AI) tools in forensics and digital forensics. The impostor biasis the tendency to doubt the authenticity or validity of the output generatedby AI tools, such as deepfakes, in the form of audio, images, and videos. Thisbias may lead to erroneous judgments or false accusations, undermining thereliability and credibility of forensic evidence. The paper discusses thepotential causes and consequences of the impostor bias, and suggests somestrategies to prevent or counteract it. By addressing these topics, this paperseeks to offer valuable insights into understanding cognitive biases inforensic practices and provide recommendations for future research andpractical applications to enhance the objectivity and validity of forensicinvestigations.

AI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions