  Recent research has highlighted a key issue in speech deepfake detection:
models trained on one set of deepfakes perform poorly on others. The question
arises: is this due to the continuously improving quality of Text-to-Speech
(TTS) models, i.e., are newer DeepFakes just 'harder' to detect? Or, is it
because deepfakes generated with one model are fundamentally different to those
generated using another model? We answer this question by decomposing the
performance gap between in-domain and out-of-domain test data into 'hardness'
and 'difference' components. Experiments performed using ASVspoof databases
indicate that the hardness component is practically negligible, with the
performance gap being attributed primarily to the difference component. This
has direct implications for real-world deepfake detection, highlighting that
merely increasing model capacity, the currently-dominant research trend, may
not effectively address the generalization challenge.
