ATTACK: Learning the Distributions of Adversarial Examples for an - PowerPoint PPT Presentation

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ATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li* 1 Lijun Li* 1 Liqiang Wang 1 Tong Zhang 2 Boqing Gong 3 *Equal Contribution 1 University of Central Florida 2 Hong

ATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Yandong Li* 1 Lijun Li* 1 Liqiang Wang 1 Tong Zhang 2 Boqing Gong 3 *Equal Contribution 1 University of Central Florida 2 Hong Kong University of Science and Technology 3 Google
Adversarial Examples Adversarial Noise + 𝜀 𝑦 ′ 𝑦 90% book jacker 82% puma
Popular: Gradient-Based Adversarial Attack Gradient of classifier output according to 𝑦 . White-box: Black-box: ➢ FGS (Goodfellow et al. 2014) ➢ ZOO (Chen et al. 2017) ➢ BPDA (Athalye et al., 2018). ➢ Query-Limited (Ilyas et al. 2018) ➢ PGD (Madry et al., 2018) ➢ … ➢ …
One? Adversarial Perturbation (F (For an an In Input) Bad local optimum, non-smooth optimization, curse of dimensionality, etc.
ATTACK Learn the distributions of adversarial examples
ATTACK Learn the distributions of adversarial examples Smoothes the optimization Higher attack success rate Reduce the “attack dimension” Less queries into the network Characterizes the risk of the input example New defense methods
ATTACK Learn the distributions of adversarial examples
ATTACK ➢ How to define the distributions of adversarial examples? ➢ Optimization : how to maximize the objective function. Poster session: Wed Jun 12th 06:30 -- 09:00 PM @ Pacific Ballroom #69
Experiments (Comparison with BPDA) 100 90 80 70 CIFAR10: ADV-TRAIN, ADV-BNN, 60 THERM-ADV, CAS-ADV, ADV-GAN, LID, 50 40 THERM, SAP, VANILLA WRESNET-32 30 20 10 IMAGENET: GUIDED DENOISER, 0 RANDOMIZATION, INPUT-TRANS, PIXEL DEFLECTION, VANILLA INCEPTION V3 BPDA NATTACK ➢ ATTACK: 100% success rate on six out of the 13 defenses and more than 90% on five of the rest . ➢ Competitive with white-box attack: BPDA (Athalye et al., 2018).
Experiments (Comparison with Black-box Approaches) 100 90 80 CIFAR10: ADV-TRAIN, THERM- 70 60 ADV, CAS-ADV, ADV-GAN, LID, 50 THERM, SAP, VANILLA WRESNET- 40 30 32 20 10 IMAGENET: RANDOMIZATION, 0 INPUT-TRANS, VANILLA INCEPTION V3 ZOO QL NATTACK ➢ The black-box baselines hinges on the quality of the estimated gradient. ➢ Fail to attack Non-smooth DNNs.
ATTACK In a nutshell, ➢ Is a powerful black-box attack, >= white-box attack. ➢ Is universal : fooled different defenses by a single algorithm . ➢ Characterize the distributions of adversarial examples. ➢ Reduce the “attack dimension” Poster session: Wed Jun 12th 06:30 -- 09:00 PM @ Pacific Ballroom #69

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