  In this work, we present a deep learning-based approach for image tampering
localization fusion. This approach is designed to combine the outcomes of
multiple image forensics algorithms and provides a fused tampering localization
map, which requires no expert knowledge and is easier to interpret by end
users. Our fusion framework includes a set of five individual tampering
localization methods for splicing localization on JPEG images. The proposed
deep learning fusion model is an adapted architecture, initially proposed for
the image restoration task, that performs multiple operations in parallel,
weighted by an attention mechanism to enable the selection of proper operations
depending on the input signals. This weighting process can be very beneficial
for cases where the input signal is very diverse, as in our case where the
output signals of multiple image forensics algorithms are combined. Evaluation
in three publicly available forensics datasets demonstrates that the
performance of the proposed approach is competitive, outperforming the
individual forensics techniques as well as another recently proposed fusion
framework in the majority of cases.
