Assessing the Effectiveness of Network Security Tools in Mitigating the Impact of Deepfakes AI on Public Trust in Media

Abstract

The rising threat of deepfake technology challenges public trust in media, necessitating robust countermeasures. This study proposes the Anti-DFK framework, a comprehensive strategy to mitigate the spread of deepfakes on major social platforms such as Instagram, Facebook, YouTube, and Twitter. The framework integrates deep learning-based detection engines, digital watermarking, and advanced network access controls, including URL filtering, domain reputation filtering, contenttype filtering, and Geo-IP blocking. Analyzing historical deepfake data, user engagement metrics, and public sentiment from Kaggle Datasets, the study employed deep learning models-CNNs, LSTMs, and Transformer-based-to evaluate detection capabilities, achieving the highest controlled environment accuracy of 0.97. Digital watermarking techniques were tested for robustness against various attacks, with the DCT method displaying significant resilience. Network access controls were assessed for their effectiveness in curtailing the spread of deepfakes, with content filtering proving the most effective by reducing dissemination by nearly 80%. Findings indicate a critical negative impact of deepfakes on public trust, underscoring the need for the integrated approach offered by the Anti-DFK framework. The study concludes that implementing these sophisticated detection tools, combined with robust digital watermarking and stringent network controls, can significantly enhance the integrity of media content and restore public confidence.

Keywords: Deepfake detection, digital watermarking, network access controls, public trust, anti-DFK framework