A dual-branch image tampering detection model based on noise and anomalous features

Researchers have developed a dual-branch image tampering detection model that uses two parallel processing paths to identify when images have been altered or forged. The model analyzes both noise patterns (statistical irregularities in pixel data) and anomalous features (unexpected or out-of-place patterns) to detect tampering, offering a more comprehensive approach than methods that examine only one type of indicator.