THE VANISHING TRICK FOR SELF-DRIVING CARS - Weilin Xu - DEF CON 26 CAAD VILLAGE
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Youtube: https://www.youtube.com/watch?v=iUwQjm8x-5w We will introduce a magic trick that vanishes objects in front of self-driving cars adversarial machine learning techniques. Weilin Xu is the intern at Baidu X-Lab, PhD candidate at the University of Virginia. Yunhan Jia is the senior security scientist at Baidu X-Lab. Zhenyu Zhong is the staff security scientist at Baidu X-Lab
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Youtube: https://www.youtube.com/watch?v=gcrqC7M5uec
Targeted attacks of image classifiers are difficult to transfer from one model to another. Only strong adversarial attacks with the knowledge of the classifier can bypass existing defenses. To defend against such attacks, we implement an “adversarial^2 training” method to strengthen the existing defenses.
Yao Zhao is an applied scientist at Microsoft AI & Research working on natural language understanding/generation and search ranking. During his Ph.D. at Yale University, he worked in the field of computuer vision and optics.
Yuzhe Zhao is a software engineer in Google Research, working on natural language understanding. He recently earned his Ph.D. from Yale University. Previously, he received his undergraduate degree in mathematics and physics from Shanghai Jiao Tong University.
HOW TO LEVERAGE THE OPEN-SOURCE INFORMATION TO MAKE AN EFFECTIVE ADVERSARIAL ...
Youtube: https://www.youtube.com/watch?v=__5H-MopPts
Adversarial attack/defense against machine learning models in digital world.
NorthWestSec, a team of independent security researchers concentrating on AI and security topic. They have demonstrated the Hacking Google reCaptcha using Deep learning technology at GeekPwn 2017 in Silicon Valley.
BOOSTING ADVERSARIAL ATTACKS WITH MOMENTUM - Tianyu Pang and Chao Du, THU - D...
Youtube: https://www.youtube.com/watch?v=Pu2WQnU0GzA
Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial at- tacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of existing adversarial attacks can only fool a black-box model with a low success rate. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. With this method, we won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.
Tianyu Pang is a first-year Ph.D. student of TSAIL Group in the Department of Computer Science and Technology, Tsinghua University, advised by Prof. Jun Zhu. His research interest includes machine learning, deep learning and their applications in computer vision, especially robustness of deep learning.
RECENT PROGRESS IN ADVERSARIAL DEEP LEARNING ATTACK AND DEFENSE - Wenbo Guo a...
Youtube: https://www.youtube.com/watch?v=i_6YSQdq-K0
In this talk, the speaker will introduce the state-of-art techniques in both defense and attack. More specifically, he will summary the most effective attack approach and the defense mechanisms. He will also share the approaches their team adopted for the competition.
Wenbo Guo is a Ph.D. student in the College of Information Science and Technology at Pennsylvania State University. Currently, he is a research intern at JD security research center in Silicon Valley. Before joining the Penn State, he got his Master degree from Shanghai Jiao Tong University in 2017. His research mainly focuses on deep learning as well as its applications in program analysis and security. He has published several research papers in the high-quality journals and conferences, such as KDD.
Alejandro Cuevas, originally from Paraguay, graduated in May 2018 from The Pennsylvania State University with a B.S. Security and Risk Analysis. As an undergraduate, Alejandro co-authored 2 papers in different areas within computer security. At Penn State, Alejandro has worked on analyzing the challenges in the reproduction of crowd-reported vulnerabilities and is currently involved in a project presenting a novel RNN for memory alias analysis. Furthermore, Alejandro has also extensively collaborated with EPFL, exploring the security challenges faced by the ICRC and helping in the deployment of an anonymous communication protocol with provable traffic-analysis resistance. Alejandro is currently applying to Ph.D. programs and hopes to start in the fall of 2019.
WEAPONS FOR DOG FIGHT：ADAPTING MALWARE TO ANTI-DETECTION BASED ON GAN - Zhuan...
Youtube: https://www.youtube.com/watch?v=SDg5FzTvyPM
Since the malware come out，there is a fight between malware and AV. So more and more methods based on machine learning apply to detect malware. We will share how to detect polymorphic malware based on CNN，then we will introduce a method use generative adversarial network to generate adversarial malware examples to bypass machine learning based detection models.
Zhuang Zhang, Bo Shi, Hangfeng Dong, from Tencent Yunding Lab(Tweet@YDLab9)
Hardware Trojan Attacks on Neural Networks - Joseph Clements - DEF CON 26 CAA...
Youtube: https://www.youtube.com/watch?v=0At0g4IIbAQ
Driven by their accessibility and ubiquity, deep learning has seen rapid growth into a variety of fields, in recent years, including many safety-critical areas. With the rising demands for computational power and speed in machine learning, there is a growing need for hardware architectures optimized for deep learning and other machine learning models, specifically in tightly constrained edge based systems. Unfortunately, the modern fabless business model of manufacturing hardware, while economic, leads to deficiencies in security through the supply chain. In addition, the embedded, distributed, unsupervised, and physically exposed nature of edge devices would make various hardware or physical attacks on edge devices as critical threats. In this talk, I will first introduce the landscape of adversarial machine learning on the edge. I will discuss several new attacks on neural networks from the hardware or physical perspective. I will then present our method for inserting a backdoor into neural networks. Our method is distinct from prior attacks in that it was generated to neither alter the weights nor inputs of a neural network. But rather, it inserts a backdoor by altering the functionality of operations implemented by the network on those parameters during the production of the neural network.
Joseph Clements, work with Dr. Yingjie Lao’s Secure and Innovative Computing Research Group conducting research
Huiming Liu-'resident evil' of smart phones--wombie attack
In this presentation, Huiming Liu, the researcher of Tencent Security Xuanwu Lab, will present an astonishing mobile wireless zombie(Wombie) attack demo — the smartphone viruses spread like zombies in “Resident Evil”, and the technique details will also be explained. The Wombie doesn’t rely on Internet to spread, so it can’t be detected on the internet. Besides, it can serve as an attack amplifier for many other attack methods. For example, the recent KRACK attack about WPA2 will benefit a lot if combined with the Wombie Attack.
Zhiyun Qian-what leaves attacker hijacking USA Today site
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Bo Li-they’ve created images that reliably fool neural network
Although deep neural networks (DNNs) perform well in a variety of applications, they are vulnerable to adversarial examples resulting from small-magnitude perturbations added to the input data. Inputs modified in this way can be mislabeled as a target class in targeted attacks or as a random class different from the ground truth in untargeted attacks. However, recent studies have demonstrated that such adversarial examples have limited effectiveness in the physical world due to changing physical conditions—they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this talk, Bo Li will introduce a general attack algorithm to take into account the numerous physical conditions and produces robust adversarial perturbations. This method captures a range of diverse physical conditions, including those encountered when images are captured from moving vehicles. We evaluate our physical attacks using this methodology and effectively fool real-world road sign classifiers.
Alexey kurakin-what's new in adversarial machine learning
– overview of adversarial examples and why they present a problem for the security
– overview of attacks and defenses.
– overview of the adversarial competition I organized and how results of the competition could provide insight into building robust machine learning classifiers.
– what will be future challenges in this area.
Nick Stephens-how does someone unlock your phone with nose
Nick Stephens presented an exploit chain that achieves complete compromise of a smartphone's trusted execution environment (TEE) from an untrusted app. The chain involves four steps: (1) exploiting vulnerabilities in the TEE driver to escalate from userland to kernel code execution, (2) exploiting parameter passing to escalate from kernel to a trustlet, (3) exploiting lack of pointer sanitization in system calls to escalate from the trustlet to the trusted core, and (4) reversing and patching the fingerprint authentication trustlet to disable fingerprint security. The talk provided technical details on vulnerabilities and exploitation techniques at each step of the chain to achieve the goal of trusted app and TEE compromise.
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PRACTICAL ADVERSARIAL ATTACKS AGAINST CHALLENGING MODELS ENVIRONMENTS - Moust...
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TRANSFERABLE ADVERSARIAL PERTURBATIONS - Tencent Blade Team - DEF CON 26 CAA...
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ADVERSARIAL^2 TRAINING - Yao Zhao, Yuzhe Zhao - DEF CON 26 CAAD VILLAGE
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BOOSTING ADVERSARIAL ATTACKS WITH MOMENTUM - Tianyu Pang and Chao Du, THU - D...
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RECENT PROGRESS IN ADVERSARIAL DEEP LEARNING ATTACK AND DEFENSE - Wenbo Guo a...
RECENT PROGRESS IN ADVERSARIAL DEEP LEARNING ATTACK AND DEFENSE - Wenbo Guo a...
WEAPONS FOR DOG FIGHT：ADAPTING MALWARE TO ANTI-DETECTION BASED ON GAN - Zhuan...
WEAPONS FOR DOG FIGHT：ADAPTING MALWARE TO ANTI-DETECTION BASED ON GAN - Zhuan...
Hardware Trojan Attacks on Neural Networks - Joseph Clements - DEF CON 26 CAA...
Hardware Trojan Attacks on Neural Networks - Joseph Clements - DEF CON 26 CAA...
Huiming Liu-'resident evil' of smart phones--wombie attack
Huiming Liu-'resident evil' of smart phones--wombie attack
Zhiyun Qian-what leaves attacker hijacking USA Today site
Zhiyun Qian-what leaves attacker hijacking USA Today site
Bo Li-they’ve created images that reliably fool neural network
Bo Li-they’ve created images that reliably fool neural network
Alexey kurakin-what's new in adversarial machine learning
Alexey kurakin-what's new in adversarial machine learning
Nick Stephens-how does someone unlock your phone with nose
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THE VANISHING TRICK FOR SELF-DRIVING CARS - Weilin Xu - DEF CON 26 CAAD VILLAGE
- 1. Magic Tricks for Self-driving Cars Weilin Xu, Zhenyu Zhong, Yunhan Jia
- 2. • This is a proof-of-concept. • We are NOT targeting at any autonomous vehicle vendors. • Don’t try to fool your neighbor’s car. AUTHORS’ WARNING
- 3. Autonomous Vehicle Framework Radar LiDAR Camera Image: https://medium.com/toyota-ai-ventures/https-medium-com-toyota-ai-ventures-announcingblackmore-7947eacc9e9e Camera
- 6. Our Target: YOLOv3 YOLO v3 Object Detection Model [147 Layers, 62M Parameters] Input [416x416x3] Output [3549 Bounding Boxes] Image: http://media.nj.com/traffic_impact/photo/all-way-stop-sign-that-flashes-in-montclairjpg-30576ab330660eff.jpg
- 7. Trained with the COCO Dataset • Common Objects in Context • 80 Classes: person, [car, truck, bus], [bicycle, motorcycle], [stop sign, traffic light], etc. Source: http://cocodataset.org/
- 8. car 0.01% YOLOv3 Inference 116x9 0 156x 198 373x326Anchor Boxes 𝑏 𝑥 = 𝜎 𝑡 𝑥 + 𝑐 𝑥 𝑏 𝑦 = 𝜎 𝑡 𝑦 + 𝑐 𝑦 𝑏 𝑤 = 𝑝 𝑤 𝑒 𝑡 𝑤 Center Point Object Size 𝑏ℎ = 𝑝ℎ 𝑒 𝑡ℎ 𝑝ℎ 𝑝 𝑤 13 x 13 Grid (𝑐 𝑥, 𝑐 𝑦) = (11,2) Prediction Vector Bounding Box 80 Class ConfidenceObjectness 𝑡 𝑥 𝑡 𝑦 𝑡 𝑤 𝑡ℎ 𝑝 𝑜𝑏𝑗 𝑐1 𝑐2 … … 𝑐79 𝑐80 ■ stop sign 99% × car 0.01% car 0.01%
- 9. Threat Model: Image Patch Attack Company Logo
- 10. Threat Model: Image Patch Attack Company Logo
- 11. Threat Model: Image Patch Attack Company Logo
- 12. Threat Model: Image Patch Attack Company Logo
- 13. Attack Algorithms • Input Construction • Objectives • Optimization
- 15. Objectives • Object Production • Object Vanish • Object Transformation
- 16. Object Production - Coarse We want more certain objects on the whole image. • Easy to implement. • May be difficult to optimize. Company Logo
- 17. Object Production - Precise We want a certain object of a specific size in a specific location. Company Logo
- 18. Object Vanish - Coarse We want a certain object class to vanish on the whole image.
- 19. Object Transformation - Coarse We want certain object class to transform to other class.
- 20. Optimization • Change of variable Convert to tanh() space to encode the [0, 1] interval constraint. Friendly to many off-the-shelf optimizers, e.g. Adam. • To optimize logits Skip sigmoid() to avoid vanishing gradient. Carlini, Nicholas, and David Wagner. “Towards Evaluating the Robustness of Neural Networks.” IEEE S&P (Oakland) 2016.
- 21. But, Image Sensing is not an Identity Function Model Input [416x416x3] ▪ Limited Resolution ▪ Distortions ▪ Random Noise ▪ … Digital Image Scene
- 22. Towards Robust Physical Adversarial Examples • [Limited Resolution] Smoother patch with the total variation regularization. • [Distortions] Color management with the non-printability loss. • [Inaccurate Patch] Random transformation during optimization iterations. • …… Sharif, Mahmood, et al. "Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition." ACM CCS 2016.
- 23. Conclusion • Magicians can fool object detection models, so can attackers. • We should be cautious with self-driving cars that rely on computer vision.