HOW TO LEVERAGE THE OPEN-SOURCE INFORMATION TO MAKE AN EFFECTIVE ADVERSARIAL ATTACK/DEFENSE AGAINST DEEP LEARNING MODEL Wei Li and Xiaojin Jiao - DEF CON 26 CAAD VILLAGE
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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.
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– 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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Bo Li-they’ve created images that reliably fool neural network
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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HOW TO LEVERAGE THE OPEN-SOURCE INFORMATION TO MAKE AN EFFECTIVE ADVERSARIAL ATTACK/DEFENSE AGAINST DEEP LEARNING MODEL Wei Li and Xiaojin Jiao - DEF CON 26 CAAD VILLAGE
- 3. •
- 5. CleverHans
- 6. Adversarially trained ImageNet models
- 7. • •
- 8. Inception V3 Adv_Inception V3 Ens_adv_Ires V2 BIM_inception 88.5% 0 0 BIM_adv_inception 0 75.7% 0 BIM_ens_adv_IRes 0 0 85.3% BIM_ens3 93.7% 76.8% 81.5% Hit-target rate
- 9. Inc: Inception V3 Ires: Inc_resnet_v2 GD: Guided Denoise RP: Random Padding
- 10. Defense Attack Ensemble adversarial training Ensemble adversarial attacking Gradient masking White-box attack + transferability Random perturbation Gradient smoothing Image processing and randomization Expectation over transformation (EOT) Detection ??
- 13. • Robust against neural-network-based adversarial attack
- 14. • Kmean filter
- 16. • •