[paper]AdvJND:Generating Adversarial Examples with Just Noticeable Difference
生成對抗樣本有兩個要求：攻擊成功率和圖像保真度指標。 增加擾動可以確保對抗樣本的攻擊成功率很高； 但是生成的對抗樣本隱蔽性很差。 爲了在攻擊成功率和圖像保真度之間取折衷，提出了一種名爲AdvJND的方法，該方法在生成對抗樣本時在失真函數的約束下添加了視覺模型係數，該係數用來衡量視覺上的差異。AdvJND算法生成的對抗樣本產生的梯度分佈與原始輸入相似。該方法可以認爲是一種輔助生成方法，用來改善生成算
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