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Joysula Rao, IBM Corporation |
- AI techniques used for defense
- AI-powered attacks
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- Approaches and methodologies for developing provable security
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Anish Athalye, Massachusetts Institute of Technology |
- The increase of the use of AI/machine learning to attack real systems
- More realistic attacks by malicious actors
- More principled evaluations of defenses
- Provable/certifiable defenses
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- Increased security of machine learning systems with answers to the following questions:
- What is an adversarial example?
- What is the specification of a machine learning system?
- How should a machine behave given a particular input?
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Terry Boult, University of Colorado, Colorado Springs |
- Open-set recognition algorithms for well-behaved, low-moderate dimensional feature spaces
- Realistic large open-set data sets/protocols
- Better understanding of image–feature relationships
- Ability of iterative Layer-wise Origin-Target Synthesis (LOTS) to attack all kinds of systems
- LOTS attacks are reasonably portable
- Use of LOTS to build physical attacks/camouflage
- Problems with good representations to relate images to features
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- Better network models for open-set deep recognition
- High-dimensional open-set algorithms
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Rama Chellappa, University of Maryland, College Park |
- Explore the robustness of deeper networks
- Work with multimodal inputs
- Increase theoretical analysis
- Investigate how humans and machines can work together to thwart adversarial attacks
- Demonstrate on more difficult computer vision problems (e.g., face verification/identification, action detection, detection of doctored media)
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- Keep changing the network configuration and parameters in a probabilistic manner with guaranteed performance (i.e., adaptive networks)
- Humans and machines work together
- Design networks that incorporate common sense reasoning
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Aram Galstyan, Information Sciences Institute, University of Southern California |
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- Hybrid sensemaking systems
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Judy Hoffman, Georgia Institute of Technology |
- Develop effective uncertainty measures and confidence intervals
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