Detecting deepfakes and false ads through analysis of text and social engineering techniques

AbstractExisting deepfake detection algorithm frequently fail to successfully identify fabricated materials. These algorithms primarily focus on technical analysis of video and audio, often neglecting the meaning of content itself. In this paper, we introduce a novel approach that emphasizes the analysis of text-based transcripts, particularly those from AI-generated deepfake advertisements, placing the text content at the center of attention. Our method combines linguistic features, evaluation of grammatical mistakes, and the identification of social engineering techniques commonly used in fraudulent content. By examining stylistic inconsistencies and manipulative language patterns, we enhance the accuracy of distinguishing between real and deepfake materials. To ensure interpretability, we employed classical machine learning models, allowing us to provide explainable insights into decision-making processes. Additionally, zero-shot evaluations were conducted using three large language model based solutions to assess their performance in detecting deepfake content. The experimental results show that these factors yield a 90% accuracy in distinguishing between deepfake-based fraudulent advertisements and real ones. This demonstrates the effectiveness of incorporating content-based analysis into deepfake detection, offering a complementary layer to existing audio-visual techniques.