Research Portfolio

Adversarial Machine Learning: Attack, Defense, and Robustness Evaluation

    • Joint text paraphrasing adversarial attacks at the word and sentence levels and adversarial training [SysML'19]
    • Detecting adversarial audio inputs using temporal dependency [ICLR'19]
    • Structured adversarial attack: spatial structure guided adversarial attack and model interpretability [ICLR'19]
    • Query-efficient zeroth-order optimization based black-box attack with limited information (decision-based model revealing only top-1 prediction label) [ICLR'19]
    • AutoZOOM [AAAI'19]: query-efficient black-box attacking acceleration via dimensional reduction and zeroth-order optimization
    • CROWN & CNN-Cert [NeurIPS'18, AAAI'19]: Formal and efficient robustness certification of neural networks with general activation functions and popular layer modules
    • Adversarial attack on sparse regression (feature identification) [GlobalSIP'18]
    • Adversarial robustness v.s. classification accuracy tradeoff uncovered from 18 deep ImageNet models + attack transferability analysis between 306 pairs of these networks [ECCV'18]
    • Show-and-Fool [ACL'18]: adversarial examples for neural image captioning systems
    • ZOO [AI-Sec'17]: powerful black-box attack to neural networks - nearly the same performance as white-box attacks
    • EAD [AAAI'18, two ICLR'18 Wksp, DSN'18 Wksp]: crafting L1 norm based adversarial examples - better attack transferability; weakened several defenses and adversary analysis
    • CLEVER [ICLR'18, GlobalSIP'18]: attack-agnostic network robustness measure - estimating certified attack lower bounds

ZOO (black-box attack via direct model queries)

[AI-Sec'17] https://arxiv.org/abs/1708.03999

EAD (L1 distortion based white-box attack)

[AAAI'18] https://arxiv.org/abs/1709.04114 [ICLR'18 Wksp] https://arxiv.org/abs/1710.10733[ICLR'18 Wksp] https://arxiv.org/abs/1803.09638[DSN'18 Wksp] https://arxiv.org/abs/1805.00310

Show-and-Fool: adversarial examples for neural image captioning systems

[ACL'18] https://arxiv.org/abs/1712.02051

AutoZOOM: query-efficient black-box adversarial attacking acceleration via dimensional reduction and zeroth-order optimization

Robustness verification and evaluation for neural nets

Accuracy v.s. robustness tradeoff of 18 ImageNet models

Adversarial attack on sparse regression

Detecting adversarial audio inputs using temporal dependency

Community Detection: Theory and Algorithms

    • Phase transition analysis of community detection under general connectivity models [T-SP, Phy. Rev. E]
    • AMOS & MIMOSA: theory-driven automated community detection algorithms for single-layer [T-SP] and multi-layer graphs [T-SIPN]
    • Deep (core) community detection [T-SP]
    • SGC-GEN: pseudo-supervised community detection meta algorithm [ICDM'17]

To be detectable, or not to be... Performance characterization of community detection

Communication detection in multi-layer networks

Event Propagation and Control in Networks

    • Modeling malware propagation in heterogeneous networks [Comm. Mag, Comm. Lett., J-IoT, T-CB, GLOBECOM'10]
    • Event propagation control via node and edge patching in communication networks [Comm. Mag.]
    • Identifying influential links on Twitter networks using network of networks model [T-SIPN]

Information propagation in heterogeneous networks

Malware propagation via multiple paths

Tweet propagation and user language fields

Network Analytics and Graph Data Mining

    • GAN-based graph generator learned from a single graph [IEEE Access]
    • Bifurcation analysis of cell reprogramming [ICASSP'18, iScience]
    • Scalable end-to-end spectral clustering using random features [KDD'18]
    • Structural feature extraction from a single graph or a graph sequence [ICASSP'16]
    • Anomaly detection based on graph connectivity

Network Resilience

    • LFVC: effective centrality measure based attack for network disruption [ICASSP'14, Comm. Mag.]
    • Sequential and game-theoretic information fusion for defending connectivity attacks [Phy. Rev. E, J-IoT]

Optimization for Machine Learning and Signal Processing

    • Zeroth-order signSGD: faster convergence of zeroth order optimization [ICLR'19]
    • Non-convex zeroth order stochastic variance reduced algorithm [NeurIPS'18]
    • Accelerated distributed dual averaging over networked agents [T-SP]
    • Zeroth-order ADMM: convergence and algorithm [AISTATS'18]

(Last updated in Mar. 2019)