Improving the Imperceptibility of Adversarial Examples Based on Weakly Perceptual Perturbation in Key Regions

Algorithm 1

WPAE algorithm.

	Initialization: T = Faster RCNN, f = Rf, dataset =  VOC 2007, batchsize = 1, epoch = 5, α = 1000, β = [0.0001, 0.0002], and γ = 1
	While t < epoch:
	for x in the dataset:
	Train G:
	G (x) ⟶ r, M (x)⟶M,
	F (p) ⟶ P
	x′ = x + P
	T (x′) ⟶
	G (x, p) ⟶ LGAN_G
	Calculate Lperceptual (x, x′), Lfeature (f,), and LDAG
	LG = LGAN_G + αLperceptual + βLfeature + γLDAG
	Update G parameters
	Train D:
	D (img_real, ones) ⟶ loss_real
	D (img_fake, zeros) ⟶ loss_ fake
	LD = (loss_real + loss_ fake)0.5
	Update D parameters
	end for
	t ⟶ t++
	end while