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Adversarial Perturbation Attacks on ML-based CAD: A Case Study on CNN-based Lithographic Hotspot Detection

https://doi.org/10.1145/3408288
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-00470-5_13openalex-snapshot
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
inferredcites →https://doi.org/10.1016/j.dsp.2017.10.011openalex-snapshot
Methods for interpreting and understanding deep neural networks
inferredcites →https://doi.org/10.1016/j.patcog.2018.07.023openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning
inferredcites →https://doi.org/10.1109/access.2019.2909068openalex-snapshot
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00175openalex-snapshot
Robust Physical-World Attacks on Deep Learning Visual Classification
inferredcites →https://doi.org/10.1109/dac.2018.8465816openalex-snapshot
GAN-OPC: Mask Optimization with Lithography-guided Generative Adversarial Nets
inferredcites →https://doi.org/10.1109/dac.2018.8465835openalex-snapshot
A Machine Learning Framework to Identify Detailed Routing Short Violations from a Placed Netlist
inferredcites →https://doi.org/10.1109/dsd.2018.00076openalex-snapshot
A Machine Learning Approach for Area Prediction of Hardware Designs from Abstract Specifications
inferredcites →https://doi.org/10.1109/iccad45719.2019.8942108openalex-snapshot
Proceedings of the 2019 International Conference on Computer-Aided Design (ICCAD)
inferredcites →https://doi.org/10.1109/iscas.2018.8351885openalex-snapshot
Deep Learning for Logic Optimization Algorithms
inferredcites →https://doi.org/10.1109/jproc.2014.2335155openalex-snapshot
A Primer on Hardware Security: Models, Methods, and Metrics
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2019.00032openalex-snapshot
Stealthy Porn: Understanding Real-World Adversarial Images for Illicit Online Promotion
inferredcites →https://doi.org/10.1109/tcad.2014.2351273openalex-snapshot
A Fuzzy-Matching Model With Grid Reduction for Lithography Hotspot Detection
inferredcites →https://doi.org/10.1109/tcad.2018.2837078openalex-snapshot
Layout Hotspot Detection With Feature Tensor Generation and Deep Biased Learning
inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-snapshot
Learning from Imbalanced Data
inferredcites →https://doi.org/10.1109/vts.2018.8368646openalex-snapshot
Enhanced hotspot detection through synthetic pattern generation and design of experiments
inferredcites →https://doi.org/10.1117/12.2085790openalex-snapshot
A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction
inferredcites →https://doi.org/10.1117/12.2515172openalex-snapshot
Hotspot detection using squish-net
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1145/2228360.2228576openalex-snapshot
Accurate process-hotspot detection using critical design rule extraction
inferredcites →https://doi.org/10.1145/2429384.2429457openalex-snapshot
ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite
inferredcites →https://doi.org/10.1145/267665openalex-snapshot
Proceedings of the 1997 international symposium on Physical design - ISPD '97
inferredcites →https://doi.org/10.1145/2976749.2978392openalex-snapshot
Accessorize to a Crime
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3061639.3062270openalex-snapshot
Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning
inferredcites →https://doi.org/10.1145/3133956.3134057openalex-snapshot
MagNet
inferredcites →https://doi.org/10.1145/3177540.3177554openalex-snapshot
Machine Learning Applications in Physical Design
inferredcites →https://doi.org/10.1145/3177540.3178242openalex-snapshot
Data Efficient Lithography Modeling with Residual Neural Networks and Transfer Learning
inferredcites →https://doi.org/10.1145/3195970.3196026openalex-snapshot
Developing synthesis flows without human knowledge
inferredcites →https://doi.org/10.1145/3240765.3240843openalex-snapshot
RouteNet
inferredcites →https://doi.org/10.1145/3240765.3243484openalex-snapshot
Machine learning for performance and power modeling of heterogeneous systems
inferredcites →https://doi.org/10.1145/3243734.3264418openalex-snapshot
Wild patterns: Ten years after the rise of adversarial machine learning half-day tutorial
inferredcites →https://doi.org/10.1145/3287624.3287684openalex-snapshot
SRAF insertion via supervised dictionary learning
inferredcites →https://doi.org/10.1145/3287624.3287685openalex-snapshot
Semi-supervised hotspot detection with self-paced multi-task learning
inferredcites →https://doi.org/10.1145/3287624.3287689openalex-snapshot
Learning-based prediction of package power delivery network quality
inferredcites →https://doi.org/10.1145/3287624.3288749openalex-snapshot
Deep learning-based framework for comprehensive mask optimization
inferredcites →https://doi.org/10.1145/3315574openalex-snapshot
CAD-Base
inferredcites →https://doi.org/10.1145/3316781.3317811openalex-snapshot
Efficient Layout Hotspot Detection via Binarized Residual Neural Network
inferredcites →https://doi.org/10.1145/3316781.3317832openalex-snapshot
GAN-SRAF
inferredcites →https://doi.org/10.14722/ndss.2018.23198openalex-snapshot
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
inferredcites →https://doi.org/10.14722/ndss.2018.23291openalex-snapshot
Trojaning Attack on Neural Networks
inferredcites →https://doi.org/10.24963/ijcai.2021/694openalex-snapshot
Adversarial Examples in Physical World
inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot
Delving into Transferable Adversarial Examples and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1705.07204openalex-snapshot
Ensemble Adversarial Training: Attacks and Defenses
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot
Towards Deep Learning Models Resistant to Adversarial Attacks
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-71522-9_1651openalex-snapshot
Hardware Security in the Age of Machine Learning
inferred← citeshttps://doi.org/10.1007/978-3-642-27739-9_1651-1openalex-snapshot
Hardware Security in the Age of Machine Learning
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103117openalex-snapshot
Detecting DDoS attacks using adversarial neural network
inferred← citeshttps://doi.org/10.1088/1361-6501/ac219aopenalex-snapshot
An imbalance aware lithography hotspot detection method based on HDAM and pre-trained GoogLeNet
inferred← citeshttps://doi.org/10.1109/asp-dac58780.2024.10473805openalex-snapshot
APPLE: An Explainer of ML Predictions on Circuit Layout at the Circuit-Element Level
inferred← citeshttps://doi.org/10.1109/dac63849.2025.11133367openalex-snapshot
Curvilinear Optical Proximity Correction via Cardinal Spline
inferred← citeshttps://doi.org/10.1109/iccad57390.2023.10323949openalex-snapshot
An Adversarial Active Sampling-Based Data Augmentation Framework for AI-Assisted Lithography Modeling
inferred← citeshttps://doi.org/10.1109/isqed57927.2023.10129359openalex-snapshot
Security and Reliability Challenges in Machine Learning for EDA: Latest Advances
inferred← citeshttps://doi.org/10.1109/mwscas48704.2020.9184612openalex-snapshot
Challenges and New Directions for AI and Hardware Security
inferred← citeshttps://doi.org/10.1109/raics61201.2024.10690072openalex-snapshot
Lithographic Hotspot Detection Using CNN
inferred← citeshttps://doi.org/10.1109/socc52499.2021.9739485openalex-snapshot
Can We Trust Machine Learning for Electronic Design Automation?
inferred← citeshttps://doi.org/10.1109/tai.2023.3340982openalex-snapshot
Manipulation Attacks on Learned Image Compression
inferred← citeshttps://doi.org/10.1109/tcad.2020.3024780openalex-snapshot
Training Data Poisoning in ML-CAD: Backdooring DL-Based Lithographic Hotspot Detectors
inferred← citeshttps://doi.org/10.1109/tcad.2020.3033749openalex-snapshot
Bias Busters: Robustifying DL-Based Lithographic Hotspot Detectors Against Backdooring Attacks
inferred← citeshttps://doi.org/10.1109/tcad.2021.3054808openalex-snapshot
Robust Deep Learning for IC Test Problems
inferred← citeshttps://doi.org/10.1109/tcad.2021.3124762openalex-snapshot
MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper
inferred← citeshttps://doi.org/10.1109/tcad.2022.3199172openalex-snapshot
The Dark Side: Security and Reliability Concerns in Machine Learning for EDA
inferred← citeshttps://doi.org/10.1109/tcad.2023.3309745openalex-snapshot
RL-OPC: Mask Optimization With Deep Reinforcement Learning
inferred← citeshttps://doi.org/10.1109/tcad.2024.3468016openalex-snapshot
Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning
inferred← citeshttps://doi.org/10.1145/3394885.3431571openalex-snapshot
Attacking a CNN-based Layout Hotspot Detector Using Group Gradient Method
inferred← citeshttps://doi.org/10.1145/3508352.3549389openalex-snapshot
Robustify ML-Based Lithography Hotspot Detectors
inferred← citeshttps://doi.org/10.1145/3566097.3567896openalex-snapshot
Rethink before Releasing Your Model
inferred← citeshttps://doi.org/10.1145/3649329.3656254openalex-snapshot
CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
inferred← citeshttps://doi.org/10.1145/3721129openalex-snapshot
LithoExp: Explainable Two-stage CNN-based Lithographic Hotspot Detection with Layout Defect Localization
inferred← citeshttps://doi.org/10.1145/3771767openalex-snapshot
You Only Need Non-Hotspot: An Unsupervised Training-Free Method for Layout Hotspot Detection
inferred← citeshttps://doi.org/10.1145/3806057openalex-snapshot
GED-Net: A Multi-modal Reconstruction Network for CAD Parametric Models based on Grouped Entity Decoding
inferred← citeshttps://doi.org/10.1155/2022/7803329openalex-snapshot
Hotspot Detection with Machine Learning Based on Pixel-Based Feature Extraction
inferred← citeshttps://doi.org/10.1609/aaai.v35i17.17743openalex-snapshot
Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images
inferred← citeshttps://doi.org/10.17762/ijritcc.v11i10s.7632openalex-snapshot
Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits
inferred← citeshttps://doi.org/10.23919/date48585.2020.9116489openalex-snapshot
Poisoning the (Data) Well in ML-Based CAD: A Case Study of Hiding Lithographic Hotspots
inferred← citeshttps://doi.org/10.23919/date51398.2021.9473966openalex-snapshot
Machine Learning Framework for Early Routability Prediction with Artificial Netlist Generator
inferred← citeshttps://doi.org/10.3390/jcp3020010openalex-snapshot
Cybersecurity for AI Systems: A Survey
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