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

Authors: Mirko Casu, Luca Guarnera, Pasquale Caponnetto, Sebastiano Battiato

Published: 2023-12-24 10:01:40+00:00

AI Summary

This paper analyzes the influence of cognitive biases, including confirmation and anchoring bias, on forensic decision-making, particularly in digital forensics with deepfakes. It introduces the "Impostor Bias," a novel tendency to systematically doubt the authenticity of multimedia content due to the proliferation of AI-generated media. The authors discuss the causes and consequences of this bias and propose strategies for its mitigation to enhance forensic investigation objectivity.

Abstract

This paper examines the impact of cognitive biases on decision-making in forensics and digital forensics, exploring biases such as confirmation bias, anchoring bias, and hindsight bias. It assesses existing methods to mitigate biases and improve decision-making, introducing the novel Impostor Bias, which arises as a systematic tendency to question the authenticity of multimedia content, such as audio, images, and videos, often assuming they are generated by AI tools. This bias goes beyond evaluators' knowledge levels, as it can lead to erroneous judgments and false accusations, undermining the reliability and credibility of forensic evidence. Impostor Bias stems from an a priori assumption rather than an objective content assessment, and its impact is expected to grow with the increasing realism of AI-generated multimedia products. The paper discusses the potential causes and consequences of Impostor Bias, suggesting strategies for prevention and counteraction. By addressing these topics, this paper aims to provide valuable insights, enhance the objectivity and validity of forensic investigations, and offer recommendations for future research and practical applications to ensure the integrity and reliability of forensic practices.


Key findings
The paper introduces the "Impostor Bias," a novel cognitive bias where individuals systematically question the authenticity of multimedia content due to the proliferation of AI-generated fakes. It highlights that traditional cognitive biases also significantly impact forensic decision-making, emphasizing the need for robust mitigation strategies. Effective deepfake detection methods, despite challenges in generalizing to new AI architectures, are essential tools to counteract these biases and ensure the objectivity of forensic investigations.
Approach
The authors define and introduce "Impostor Bias," a new cognitive bias where evaluators inherently distrust multimedia content due to the prevalence of AI-generated fakes. They analyze the impact of this and other cognitive biases on forensic investigations and review existing deepfake detection technologies and bias mitigation strategies to address these challenges.
Datasets
FFHQ (Flickr-Faces-HQ), CelebA, ImageNet, FaceForensics++, UADFV, MSCOCO, Flickr30k
Model(s)
UNKNOWN
Author countries
Italy