Deepfake Detection and AI’s Role in Preventing Digital Fraud
DOI:
https://doi.org/10.15662/IJRAI.2024.0704011Keywords:
Deepfake detection, Digital fraud prevention, Machine learning, Computer vision, Multimedia forensics, Biometric authentication, Real-time media verificationAbstract
The given research article focuses on the way in which AI-enhanced deepfake detection is turning into a first-line defense against digital fraud when synthetic media becomes increasingly believable and available. It describes that the deepfakes, a highly realistic and yet forged video, image, and audio content, permit an extensive variety of harms, among them misinformation campaigns, identity theft, impersonation attacks, financial frauds, and reputational losses. The paper brings out critical technical methods employed in current detection systems such as machine learning, computer vision and multimedia forensics to examine slight traces that may go unnoticed by human beings. Such artifacts consist of abnormal facial micro-expressions, light and shadows, temporal flicker, and combination errors on facial boundaries and discrepancies between audio and lip movement. Through the training on large datasets of authentic and manipulated media, AI models understand discriminators and therefore can indicate suspicious content almost instantly, which can be used to intervene quickly.
Its article also describes industry-specific implementations: banking and fintech to prevent transaction and impersonation frauds; cybersecurity and identity protection to perform biometric and voice analysis; media and journalism to perform authenticity verification; legal and compliance to perform effective forensic validation of evidence; and e-commerce and social platforms to identify and eliminate manipulated content by users. In quantitative terms, the research findings include performance measures with accuracy in detection of up to 98 percent, loss of money in financial fraud annually up to 60 percent, detection in a few seconds, and the reduction of false positive to 40 percent as a result of AI-human cooperation. Lastly, it talks about the endemic issues through the development of deepfakes and its evolution, privacy and ethical limitations, explainability and trust, and computational limits, by asserting that combined human regulation and AI adaptable pipelines are the scheme to sturdy, scalable deepfake defenses.
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