arXiv:2607.06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors. Despite their widespread use, these paradigms are rarely evaluated under a common protocol, making direct comparison difficult. We introduce VendorBench-100, a cross-paradigm benchmark that evaluates 36 representative models using a single adversarial 100-image corpus, a unified output schema, and a common evaluation framework. To ensure reliable assessment under the corpus
The proliferation of deepfake technology across various modalities necessitates robust and comparable detection methods to maintain digital trust and security.
A unified benchmark standardizes the evaluation of deepfake detection systems, enhancing their reliability and enabling better-informed deployment decisions across industries and governments.
The ability to objectively compare and select deepfake detection solutions, irrespective of their underlying paradigm, significantly improves.
- · Security software vendors
- · Social media platforms
- · Government agencies investigating digital fraud
- · Organizations relying on digital identity verification
- · Deepfake creators
- · Providers of unproven or ineffective detection solutions
- · Bad actors exploiting synthetic media
Improved deepfake detection capabilities lead to higher confidence in digital content authenticity.
Enhanced detection may spur further innovation in adversarial deepfake generation techniques, creating an arms race.
The benchmark could become a de facto standard, influencing research directions and market consolidation for deepfake countermeasures.
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Read at arXiv cs.AI