  Recent image manipulation localization and detection techniques usually
leverage forensic artifacts and traces that are produced by a noise-sensitive
filter, such as SRM and Bayar convolution. In this paper, we showcase that
different filters commonly used in such approaches excel at unveiling different
types of manipulations and provide complementary forensic traces. Thus, we
explore ways of merging the outputs of such filters and aim to leverage the
complementary nature of the artifacts produced to perform image manipulation
localization and detection (IMLD). We propose two distinct methods: one that
produces independent features from each forensic filter and then fuses them
(this is referred to as late fusion) and one that performs early mixing of
different modal outputs and produces early combined features (this is referred
to as early fusion). We demonstrate that both approaches achieve competitive
performance for both image manipulation localization and detection,
outperforming state-of-the-art models across several datasets.
