35 citations · 65 across the 6 of their papers we have counts for
6 papers
MALICE: Manipulation Attacks on Learned Image ComprEssion
Kang Liu, Di Wu, Yiru Wang +3
Deep learning techniques have shown promising results in image compression, with competitive bitrate and image reconstruction quality from compressed latent. However, while image c…
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images
Kang Liu, Benjamin Tan, Siddharth Garg
Unprecedented data collection and sharing have exacerbated privacy concerns and led to increasing interest in privacy-preserving tools that remove sensitive attributes from images…
Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks
Kang Liu, Benjamin Tan, Gaurav Rajavendra Reddy +3
Deep learning (DL) offers potential improvements throughout the CAD tool-flow, one promising application being lithographic hotspot detection. However, DL techniques have been show…
NNoculation: Catching BadNets in the Wild
Akshaj Kumar Veldanda, Kang Liu, Benjamin Tan +5
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdo…
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection
Kang Liu, Haoyu Yang, Yuzhe Ma +5
There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep lear…
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Kang Liu, Brendan Dolan-Gavitt, Siddharth Garg
Deep neural networks (DNNs) provide excellent performance across a wide range of classification tasks, but their training requires high computational resources and is often outsour…