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This paper evaluates 17 face image quality assessment methods as training-free signals for morphing attack detection across 20 attack sets, finding that DifFIQA(R) performs best on FRLL while many methods paradoxically assign higher quality to morphs than bona fide samples, and results transfer poorly across datasets.