  Recently, fake images generated by artificial intelligence (AI) models have
become indistinguishable from the real, exerting new challenges for fake image
detection models. To this extent, simple binary judgments of real or fake seem
less convincing and credible due to the absence of human-understandable
explanations. Fortunately, Large Multimodal Models (LMMs) bring possibilities
to materialize the judgment process while their performance remains
undetermined. Therefore, we propose FakeBench, the first-of-a-kind benchmark
towards transparent defake, consisting of fake images with human language
descriptions on forgery signs. FakeBench gropes for two open questions of LMMs:
(1) can LMMs distinguish fake images generated by AI, and (2) how do LMMs
distinguish fake images? In specific, we construct the FakeClass dataset with
6k diverse-sourced fake and real images, each equipped with a Question&Answer
pair concerning the authenticity of images, which are utilized to benchmark the
detection ability. To examine the reasoning and interpretation abilities of
LMMs, we present the FakeClue dataset, consisting of 15k pieces of descriptions
on the telltale clues revealing the falsification of fake images. Besides, we
construct the FakeQA to measure the LMMs' open-question answering ability on
fine-grained authenticity-relevant aspects. Our experimental results discover
that current LMMs possess moderate identification ability, preliminary
interpretation and reasoning ability, and passable open-question answering
ability for image defake. The FakeBench will be made publicly available soon.
