DETER: Detecting Edited Regions for Deterring Generative Manipulations
Sai Wang Ye Zhu Ruoyu Wang Amaya Dharmasiri Olga Russakovsky Yu Wu
Generative AI capabilities have grown substantially in recent years, raising
renewed concerns about potential malicious use of generated data, or "deep
fakes". However, deep fake datasets have not kept up with generative AI
advancements sufficiently to enable the development of deep fake detection
technology which can meaningfully alert human users in real-world settings.
Existing datasets typically use GAN-based models and introduce spurious
correlations by always editing similar face regions. To counteract the
shortcomings, we introduce DETER, a large-scale dataset for DETEcting edited
image Regions and deterring modern advanced generative manipulations. DETER
includes 300,000 images manipulated by four state-of-the-art generators with
three editing operations: face swapping (a standard coarse image manipulation),
inpainting (a novel manipulation for deep fake datasets), and attribute editing
(a subtle fine-grained manipulation). While face swapping and attribute editing
are performed on similar face regions such as eyes and nose, the inpainting
operation can be performed on random image regions, removing the spurious
correlations of previous datasets. Careful image post-processing is performed
to ensure deep fakes in DETER look realistic, and human studies confirm that
human deep fake detection rate on DETER is 20.4% lower than on other fake
datasets. Equipped with the dataset, we conduct extensive experiments and
break-down analysis using our rich annotations and improved benchmark
protocols, revealing future directions and the next set of challenges in
developing reliable regional fake detection models.