  Recent advancements in diffusion models have enabled the generation of
realistic deepfakes from textual prompts in natural language. While these
models have numerous benefits across various sectors, they have also raised
concerns about the potential misuse of fake images and cast new pressures on
fake image detection. In this work, we pioneer a systematic study on deepfake
detection generated by state-of-the-art diffusion models. Firstly, we conduct a
comprehensive analysis of the performance of contrastive and
classification-based visual features, respectively extracted from CLIP-based
models and ResNet or ViT-based architectures trained on image classification
datasets. Our results demonstrate that fake images share common low-level cues,
which render them easily recognizable. Further, we devise a multimodal setting
wherein fake images are synthesized by different textual captions, which are
used as seeds for a generator. Under this setting, we quantify the performance
of fake detection strategies and introduce a contrastive-based disentangling
method that lets us analyze the role of the semantics of textual descriptions
and low-level perceptual cues. Finally, we release a new dataset, called
COCOFake, containing about 1.2M images generated from the original COCO
image-caption pairs using two recent text-to-image diffusion models, namely
Stable Diffusion v1.4 and v2.0.
