Existing 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.

Deep learning has significantly enhanced voice synthesis, yielding realistic audio capable of mimicking individual voices. This progress, however, raises security concerns due to the potential misuse of audio deepfakes. Our research examines the effects of deepfakes on speaker recognition systems across English and Polish corpora, assessing both Text-to-Speech and Voice Conversion methods. We focus on the biometric similarity’s role in the effectiveness of impersonations and find that synthetic voices can maintain personal traits, posing risks of unauthorized access. The study’s key contributions include analyzing voice synthesis across languages, evaluating biometric resemblance in voice conversion, and contrasting Text-to-Speech and Voice Conversion paradigms. These insights emphasize the need for improved biometric security against audio deepfake threats.