The primary objective of GDAS is to identify and analyze any changes or alterations within a given photo. By employing traditional image forensics algorithms, we can scrutinize various visual cues, such as pixel-level inconsistencies, noise patterns, and irregularities in lighting and shadows. These conventional techniques enable us to detect common forms of tampering, including content modification, object removal or insertion, and image splicing.

In addition to traditional algorithms, GDAS incorporates state-of-the-art AI algorithms to tackle the growing threat of deepfake manipulation. Deepfake technology has become increasingly sophisticated, making it challenging to distinguish between genuine and manipulated images. However, our AI-based algorithms are specifically trained to detect the presence of deepfake elements. By leveraging deep learning and computer vision techniques, our system can analyze intricate details, facial expressions, and anomalies that indicate the likelihood of a deepfake manipulation.

GDAS, Forensic Photo Analysis System serves as a crucial tool for forensic investigators, law enforcement agencies, and media professionals who rely on accurate and trustworthy visual evidence. By providing robust analysis and detection capabilities, GDAS, assists in preserving the integrity of digital content, maintaining public trust, and ensuring the reliability of forensic investigations.