  Among the image forensic issues investigated in the last few years, great
attention has been devoted to blind camera model attribution. This refers to
the problem of detecting which camera model has been used to acquire an image
by only exploiting pixel information. Solving this problem has great impact on
image integrity assessment as well as on authenticity verification. Recent
advancements that use convolutional neural networks (CNNs) in the media
forensic field have enabled camera model attribution methods to work well even
on small image patches. These improvements are also important for determining
forgery localization. Some patches of an image may not contain enough
information related to the camera model (e.g., saturated patches). In this
paper, we propose a CNN-based solution to estimate the camera model attribution
reliability of a given image patch. We show that we can estimate a
reliability-map indicating which portions of the image contain reliable camera
traces. Testing using a well known dataset confirms that by using this
information, it is possible to increase small patch camera model attribution
accuracy by more than 8% on a single patch.
