Skip to Main Content
We propose in this paper a novel technique to correlate statistical image noise features with three EXchangeable Image File format (EXIF) header features for manipulation detection. By formulating each EXIF feature as a weighted sum of selected statistical image noise features using sequential floating forward selection, the weights are then solved as a least squares solution for modeling the correlation between the intact image and the corresponding EXIF header. Image manipulations like brightness and contrast adjustment can affect these noise features and lead to enlarged numerical difference between each actual and its estimated EXIF feature from the noise features. By using the numerical difference as a manipulation indicator, we achieve excellent performance in detecting common brightness and contrast adjustment. Based on cameras of different brands, our manipulation detection is also demonstrated to work well in a blind mode, where the camera brand/model source is unavailable. Several detection examples suggest that our model can be applied in detecting real-world forgeries.