  Recent improvements in generative AI made synthesizing fake images easy; as
they can be used to cause harm, it is crucial to develop accurate techniques to
identify them. This paper introduces "Locally Aware Deepfake Detection
Algorithm" (LaDeDa), that accepts a single 9x9 image patch and outputs its
deepfake score. The image deepfake score is the pooled score of its patches.
With merely patch-level information, LaDeDa significantly improves over the
state-of-the-art, achieving around 99% mAP on current benchmarks. Owing to the
patch-level structure of LaDeDa, we hypothesize that the generation artifacts
can be detected by a simple model. We therefore distill LaDeDa into
Tiny-LaDeDa, a highly efficient model consisting of only 4 convolutional
layers. Remarkably, Tiny-LaDeDa has 375x fewer FLOPs and is 10,000x more
parameter-efficient than LaDeDa, allowing it to run efficiently on edge devices
with a minor decrease in accuracy. These almost-perfect scores raise the
question: is the task of deepfake detection close to being solved? Perhaps
surprisingly, our investigation reveals that current training protocols prevent
methods from generalizing to real-world deepfakes extracted from social media.
To address this issue, we introduce WildRF, a new deepfake detection dataset
curated from several popular social networks. Our method achieves the top
performance of 93.7% mAP on WildRF, however the large gap from perfect accuracy
shows that reliable real-world deepfake detection is still unsolved.
