  Full face synthesis and partial face manipulation by virtue of the generative
adversarial networks (GANs) and its variants have raised wide public concerns.
In the multi-media forensics area, detecting and ultimately locating the image
forgery has become an imperative task. In this work, we investigate the
architecture of existing GAN-based face manipulation methods and observe that
the imperfection of upsampling methods therewithin could be served as an
important asset for GAN-synthesized fake image detection and forgery
localization. Based on this basic observation, we have proposed a novel
approach, termed FakeLocator, to obtain high localization accuracy, at full
resolution, on manipulated facial images. To the best of our knowledge, this is
the very first attempt to solve the GAN-based fake localization problem with a
gray-scale fakeness map that preserves more information of fake regions. To
improve the universality of FakeLocator across multifarious facial attributes,
we introduce an attention mechanism to guide the training of the model. To
improve the universality of FakeLocator across different DeepFake methods, we
propose partial data augmentation and single sample clustering on the training
images. Experimental results on popular FaceForensics++, DFFD datasets and
seven different state-of-the-art GAN-based face generation methods have shown
the effectiveness of our method. Compared with the baselines, our method
performs better on various metrics. Moreover, the proposed method is robust
against various real-world facial image degradations such as JPEG compression,
low-resolution, noise, and blur.
