Distinguishing manipulated from real images is becoming increasingly
difficult as new sophisticated image forgery approaches come out by the day.
Naive classification approaches based on Convolutional Neural Networks (CNNs)
show excellent performance in detecting image manipulations when they are
trained on a specific forgery method. However, on examples from unseen
manipulation approaches, their performance drops significantly. To address this
limitation in transferability, we introduce Forensic-Transfer (FT). We devise a
learning-based forensic detector which adapts well to new domains, i.e., novel
manipulation methods and can handle scenarios where only a handful of fake
examples are available during training. To this end, we learn a forensic
embedding based on a novel autoencoder-based architecture that can be used to
distinguish between real and fake imagery. The learned embedding acts as a form
of anomaly detector; namely, an image manipulated from an unseen method will be
detected as fake provided it maps sufficiently far away from the cluster of
real images. Comparing to prior works, FT shows significant improvements in
transferability, which we demonstrate in a series of experiments on
cutting-edge benchmarks. For instance, on unseen examples, we achieve up to 85%
in terms of accuracy, and with only a handful of seen examples, our performance
already reaches around 95%.

在图像伪造不断更新的现今，卷积神经网络和自编码结构的 Forensic-Transfer 方法可以提高图像伪造检测的可迁移性并显著提高性能。