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Among the image forensic issues investigated in the last few years, greatattention has been devoted to blind camera model attribution. This refers tothe problem of detecting which camera model has been used to acquire an imageby only exploiting pixel information. Solving this problem has great impact onimage integrity assessment as well as on authenticity verification. Recentadvancements that use convolutional neural networks (CNNs) in the mediaforensic field have enabled camera model attribution methods to work well evenon small image patches. These improvements are also important for determiningforgery localization. Some patches of an image may not contain enoughinformation related to the camera model (e.g., saturated patches). In thispaper, we propose a CNN-based solution to estimate the camera model attributionreliability of a given image patch. We show that we can estimate areliability-map indicating which portions of the image contain reliable cameratraces. Testing using a well known dataset confirms that by using thisinformation, it is possible to increase small patch camera model attributionaccuracy by more than 8% on a single patch.

Reliability Map Estimation For CNN-Based Camera Model Attribution