  As an essential processing step in computer vision applications, image
resizing or scaling, more specifically downsampling, has to be applied before
feeding a normally large image into a convolutional neural network (CNN) model
because CNN models typically take small fixed-size images as inputs. However,
image scaling functions could be adversarially abused to perform a newly
revealed attack called image-scaling attack, which can affect a wide range of
computer vision applications building upon image-scaling functions.
  This work presents an image-scaling attack detection framework, termed as
Decamouflage. Decamouflage consists of three independent detection methods: (1)
rescaling, (2) filtering/pooling, and (3) steganalysis. While each of these
three methods is efficient standalone, they can work in an ensemble manner not
only to improve the detection accuracy but also to harden potential adaptive
attacks. Decamouflage has a pre-determined detection threshold that is generic.
More precisely, as we have validated, the threshold determined from one dataset
is also applicable to other different datasets. Extensive experiments show that
Decamouflage achieves detection accuracy of 99.9\% and 99.8\% in the white-box
(with the knowledge of attack algorithms) and the black-box (without the
knowledge of attack algorithms) settings, respectively. To corroborate the
efficiency of Decamouflage, we have also measured its run-time overhead on a
personal PC with an i5 CPU and found that Decamouflage can detect image-scaling
attacks in milliseconds. Overall, Decamouflage can accurately detect image
scaling attacks in both white-box and black-box settings with acceptable
run-time overhead.
