  Deep convolutional neural networks have achieved exceptional results on
multiple detection and recognition tasks. However, the performance of such
detectors are often evaluated in public benchmarks under constrained and
non-realistic situations. The impact of conventional distortions and processing
operations found in imaging workflows such as compression, noise, and
enhancement are not sufficiently studied. Currently, only a few researches have
been done to improve the detector robustness to unseen perturbations. This
paper proposes a more effective data augmentation scheme based on real-world
image degradation process. This novel technique is deployed for deepfake
detection tasks and has been evaluated by a more realistic assessment
framework. Extensive experiments show that the proposed data augmentation
scheme improves generalization ability to unpredictable data distortions and
unseen datasets.
