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Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability
Zijian Ying, Qianmu Li, Tao Wang +3
Various methods try to enhance adversarial transferability by improving the generalization from different perspectives. In this paper, we rethink the optimization process and propo…
Boost Adversarial Transferability by Uniform Scale and Mix Mask Method
Tao Wang, Zijian Ying, Qianmu Li +1
Adversarial examples generated from surrogate models often possess the ability to deceive other black-box models, a property known as transferability. Recent research has focused o…
Real-centric Consistency Learning for Deepfake Detection
Ruiqi Zha, Zhichao Lian, Qianmu Li +1
Most of previous deepfake detection researches bent their efforts to describe and discriminate artifacts in human perceptible ways, which leave a bias in the learned networks of ig…
Understanding CNNs from excitations
Zijian Ying, Qianmu Li, Zhichao Lian +3
Saliency maps have proven to be a highly efficacious approach for explicating the decisions of Convolutional Neural Networks. However, extant methodologies predominantly rely on gr…