Deepfakes Detection System
Mrs. R.Lavanya1, M.Praviraj2, V.Anusha3, V.Sai Teja4, P.Suresh5
1 Mrs. R.Lavanya (assistant professor)
2M.Praviraj Department of Computer Science and Engineering (Joginpally B.R Engineering College)
3V.Anusha Department of Computer Science and Engineering (Joginpally B.R EngineeringCollege)
4 V.Sai Teja Department of Computer Science and Engineering (Joginpally B.R Engineering College)
5P.Suresh Department of Computer Science and Engineering (Joginpally B.R Engineering College)
ABSTRACT
Deepfake detection systems have become critical in combating the growing misuse of synthetic media, which leverages advanced AI techniques to manipulate video, audio, and images. These systems aim to identify and differentiate genuine content from altered or artificially generated media by employing various machine learning and deep learning algorithms. Key approaches include analyzing inconsistencies in visual artifacts, facial movements, audio patterns, and spatiotemporal features that are often overlooked by human perception. As deepfake technology becomes increasingly sophisticated, detection systems must adapt by integrating robust, scalable, and real-time capabilities to maintain accuracy. Advanced detection models often rely on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures to analyze deepfake data at a granular level. Features such as unnatural blinking patterns, irregular lighting, or mismatched lip movements are examined to detect anomalies. Additionally, audio-visual cross-modal techniques are being developed to analyze the synchronization between audio and visual components. The rise of adversarial attacks on detection systems also necessitates ongoing improvements, such as incorporating adversarial training to enhance resilience. Collaboration between researchers, tech companies, and policymakers is essential to standardize benchmarks and datasets for evaluating detection methods
