  Forensic analysis of manipulated pixels requires the identification of
various hidden and subtle features from images. Conventional image recognition
models generally fail at this task because they are biased and more attentive
toward the dominant local and spatial features. In this paper, we propose a
novel Gated Context Attention Network (GCA-Net) that utilizes non-local
attention in conjunction with a gating mechanism in order to capture the finer
image discrepancies and better identify forged regions. The proposed framework
uses high dimensional embeddings to filter and aggregate the relevant context
from coarse feature maps at various stages of the decoding process. This
improves the network's understanding of global differences and reduces
false-positive localizations. Our evaluation on standard image forensic
benchmarks shows that GCA-Net can both compete against and improve over
state-of-the-art networks by an average of 4.7% AUC. Additional ablation
studies also demonstrate the method's robustness against attributions and
resilience to false-positive predictions.
