Detecting CNN-Generated Facial Images in Real-World Scenarios
- URL: http://arxiv.org/abs/2005.05632v1
- Date: Tue, 12 May 2020 09:18:28 GMT
- Title: Detecting CNN-Generated Facial Images in Real-World Scenarios
- Authors: Nils Hulzebosch, Sarah Ibrahimi, Marcel Worring
- Abstract summary: We present a framework for evaluating detection methods under real-world conditions.
We also evaluate state-of-the-art detection methods using the proposed framework.
Our results suggest that CNN-based detection methods are not yet robust enough to be used in real-world scenarios.
- Score: 15.755089410308647
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial, CNN-generated images are now of such high quality that humans
have trouble distinguishing them from real images. Several algorithmic
detection methods have been proposed, but these appear to generalize poorly to
data from unknown sources, making them infeasible for real-world scenarios. In
this work, we present a framework for evaluating detection methods under
real-world conditions, consisting of cross-model, cross-data, and
post-processing evaluation, and we evaluate state-of-the-art detection methods
using the proposed framework. Furthermore, we examine the usefulness of
commonly used image pre-processing methods. Lastly, we evaluate human
performance on detecting CNN-generated images, along with factors that
influence this performance, by conducting an online survey. Our results suggest
that CNN-based detection methods are not yet robust enough to be used in
real-world scenarios.
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