Combining Machine Learning Defenses without Conflicts | Paper Citation Record

Observation 8ac0c9bb-39a2-41eb-bcda-8ce08252c49a · outbound

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Combining Machine Learning Defenses without Conflicts Papernot et al., `` SoK : Security and privacy in machine learning,'' in EuroS&P, 2018, pp

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Combining Machine Learning Defenses without Conflicts Tian et al., ``A comprehensive survey on poisoning attacks and countermeasures in machine learning,'' ACM Computing Surveys, vol

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Combining Machine Learning Defenses without Conflicts De Cristofaro, ``A critical overview of privacy in machine learning,'' IEEE Security & Privacy, vol

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Combining Machine Learning Defenses without Conflicts Mehrabi et al., ``A survey on bias and fairness in machine learning,'' ACM Computing Surveys, vol

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Combining Machine Learning Defenses without Conflicts Pessach and E

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Observation aa689f1b-5b2d-44a1-8c3b-1d335c392119 · outbound

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Combining Machine Learning Defenses without Conflicts Li et al., `` SoK : Certified robustness for deep neural networks,'' in SP, 2023, pp

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Observation c5a1838e-f72e-4bb8-857e-cadcb08b3f45 · outbound

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Combining Machine Learning Defenses without Conflicts Duddu et al., `` SoK : Unintended interactions among machine learning defenses and risks,'' SP, 2024

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Combining Machine Learning Defenses without Conflicts Szyller and N

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Observation e4dc5df9-4e13-4c49-a7cd-1efc263ec7b9 · outbound

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Combining Machine Learning Defenses without Conflicts Gittens et al., ``An adversarial perspective on accuracy, robustness, fairness, and privacy: Multilateral-tradeoffs in trustworthy ml,'' IEEE Access, vol

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Observation 9406c10e-e95b-4b54-8fa9-75cc04cab38e · outbound

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Combining Machine Learning Defenses without Conflicts Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives

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Observation 18c143ad-d5c4-4c11-95ff-49ba28600d9d · outbound

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Combining Machine Learning Defenses without Conflicts Datta, D

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Observation c8f9439d-2545-4a41-92d4-255e14938a59 · outbound

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Combining Machine Learning Defenses without Conflicts Alves et al., ``Survey on fairness notions and related tensions,'' in EURO Journal on Decision Processes, 2023

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Observation cf073b18-b7ed-4521-8e6b-b3044c5dd219 · outbound

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Combining Machine Learning Defenses without Conflicts Chen et al., ``Privacy and fairness in federated learning: On the perspective of tradeoff,'' ACM Computing Surveys, vol

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Observation b8e8739a-60e3-477a-94d7-43662717ed12 · outbound

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Combining Machine Learning Defenses without Conflicts Fioretto et al., ``Differential privacy and fairness in decisions and learning tasks: A survey,'' in IJCAI, 2022, pp

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Combining Machine Learning Defenses without Conflicts Noppel and C

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Combining Machine Learning Defenses without Conflicts Ferry, U

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Observation b35ef26a-60c0-44bf-9b84-4f6a7fb862e2 · outbound

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Combining Machine Learning Defenses without Conflicts Yaghini et al., ``Learning with impartiality to walk on the pareto frontier of fairness, privacy, and utility,'' in Workshop on Regulatable ML@NeurIPS, 2023

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Observation 5090ef34-418b-4cab-94bf-9d17f126368c · outbound

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Combining Machine Learning Defenses without Conflicts Madry et al., ``Towards deep learning models resistant to adversarial attacks,'' in ICLR, 2018

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Observation b86300fc-f0ff-4fe6-a1cb-445337bf97bc · outbound

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Combining Machine Learning Defenses without Conflicts Zhang et al., ``mixup: Beyond empirical risk minimization,'' in ICLR, 2018

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Observation 261bad12-dab7-4b84-a03a-35de4f190b27 · outbound

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Combining Machine Learning Defenses without Conflicts Improved Regularization of Convolutional Neural Networks with Cutout

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Observation 72cbcc8c-545a-454b-be2a-897f167980dc · outbound

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Combining Machine Learning Defenses without Conflicts Rebuffi et al., ``Data augmentation can improve robustness,'' in NeurIPS, 2021, pp

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Combining Machine Learning Defenses without Conflicts Zhang et al., ``Theoretically principled trade-off between robustness and accuracy,'' in ICML, 2019, pp

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Combining Machine Learning Defenses without Conflicts Cohen et al., ``Certified adversarial robustness via randomized smoothing,'' in ICML, 2019, pp

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Combining Machine Learning Defenses without Conflicts Lecuyer et al., ``Certified robustness to adversarial examples with differential privacy,'' in SP, 2019, pp

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Combining Machine Learning Defenses without Conflicts Tsipras et al., ``Robustness may be at odds with accuracy,'' in ICLR, 2019

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Observation fbfd37d8-8df7-4554-b43b-4dd789d3f0c3 · outbound

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Combining Machine Learning Defenses without Conflicts Nie et al., ``Diffusion models for adversarial purification,'' in ICML, 2022

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Observation 6f838a98-c636-4d18-baa3-783f6d989130 · outbound

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Combining Machine Learning Defenses without Conflicts Song et al., ``Pixeldefend: Leveraging generative models to understand and defend against adversarial examples,'' in ICLR, 2018

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Combining Machine Learning Defenses without Conflicts Buckman et al., ``Thermometer encoding: One hot way to resist adversarial examples,'' in ICLR, 2018

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Combining Machine Learning Defenses without Conflicts Guo et al., ``Countering adversarial images using input transformations,'' in ICLR, 2018

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Combining Machine Learning Defenses without Conflicts Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

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Combining Machine Learning Defenses without Conflicts Tran et al., ``Spectral signatures in backdoor attacks,'' in NeurIPS, 2018, p

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Combining Machine Learning Defenses without Conflicts Chen et al., ``Detecting backdoor attacks on deep neural networks by activation clustering,'' in SafeAI@AAAI, 2018

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Combining Machine Learning Defenses without Conflicts Borgnia et al., ``Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff,'' in ICASSP, 2021, pp

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Combining Machine Learning Defenses without Conflicts Qiu et al., ``Deepsweep: An evaluation framework for mitigating dnn backdoor attacks using data augmentation,'' in AsiaCCS, 2021, p

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Combining Machine Learning Defenses without Conflicts Diakonikolas et al., ``Sever: A robust meta-algorithm for stochastic optimization,'' in ICML, 2019, pp

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Combining Machine Learning Defenses without Conflicts Liu and H

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Combining Machine Learning Defenses without Conflicts Liu et al., ``Fine-pruning: Defending against backdooring attacks on deep neural networks,'' in RAID, 2018, pp

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Combining Machine Learning Defenses without Conflicts Wu and Y

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Combining Machine Learning Defenses without Conflicts Zheng et al., ``Pre-activation distributions expose backdoor neurons,'' in NeurIPS, 2022

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Combining Machine Learning Defenses without Conflicts 175--191

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Combining Machine Learning Defenses without Conflicts Li et al., ``Reconstructive neuron pruning for backdoor defense,'' in ICML, 2023, pp

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Combining Machine Learning Defenses without Conflicts Orekondy et al., ``Knockoff nets: Stealing functionality of black-box models,'' in CVPR, 2019, pp

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Combining Machine Learning Defenses without Conflicts Adi et al., ``Turning your weakness into a strength: Watermarking deep neural networks by backdooring,'' in USENIX Security, 2018, pp

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Observation af63f2dc-ddca-42a8-b0f5-a77c1eae8635 · outbound

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Observation a37ff1c4-12ac-4a3f-97de-bbe714345eb6 · outbound

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Combining Machine Learning Defenses without Conflicts Jia et al., ``Entangled watermarks as a defense against model extraction,'' in USENIX Security, 2021, pp

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Observation 30f80730-ade6-41aa-8795-6399e1f7d7de · outbound

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Combining Machine Learning Defenses without Conflicts Uchida et al., ``Embedding watermarks into deep neural networks,'' in ICMR, 2017, p

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Observation 41d3b646-d1e7-46bf-b27c-efe27d2580ff · outbound

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Combining Machine Learning Defenses without Conflicts Bansal et al., ``Certified neural network watermarks with randomized smoothing,'' in ICML, 2022, pp

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Observation a6e0f282-5f49-40cb-882e-f21f33c77cf3 · outbound

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Combining Machine Learning Defenses without Conflicts Bagdasaryan and V

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Observation 9f21b925-1904-4d11-a0bc-2a217e5d1a53 · outbound

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Observation 07c9b905-876f-4de2-a4db-318bd9d79496 · outbound

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Observation 86049648-5417-417b-8d1b-02f8853f0b99 · outbound

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Observation 76707076-d4ff-422d-9b6e-fb9757114c2d · outbound

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Observation ed765983-042c-4d51-804d-20b1451bfa18 · outbound

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Observation 3e4a7a31-e52d-49e4-8cd8-0d4de3df74eb · outbound

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Observation 9dd9f144-3511-434c-a99e-9e2ac6349eea · outbound

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Observation 7d896771-ff24-4c48-8da3-8bdc3e6202aa · outbound

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Observation c6cb648d-8912-4438-8ebd-e2d5f3e9a7c5 · outbound

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Observation 9dd85a46-2c6c-42ed-a73e-ed714d342ccf · outbound

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Observation 3b536aff-f7c7-47f0-82af-15bbb72e8408 · outbound

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Observation 6ac4f318-cbe0-4e4c-9c18-9138fe139e8d · outbound

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Observation 63c7273c-a5c2-4152-95f6-ad4edbf858d5 · outbound

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Combining Machine Learning Defenses without Conflicts Abadi et al., ``Deep learning with differential privacy,'' in CCS, 2016, pp

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Observation f56d328b-1592-41a9-91e9-c5a7ace42686 · outbound

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Combining Machine Learning Defenses without Conflicts Hu et al., `` SoK : Privacy-preserving data synthesis,'' in SP, 2024

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Observation b56ebd0b-19f2-4e91-afe1-bdb205b9b950 · outbound

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Combining Machine Learning Defenses without Conflicts Differentially Private Generative Adversarial Network

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Observation 09099b20-bfa8-40f8-a7aa-73a33a299ded · outbound

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Observation c2d89b87-ffa8-451a-a23c-923a57dbd7e9 · outbound

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Combining Machine Learning Defenses without Conflicts Zheng and B

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Observation c914f13c-1ae8-44ee-81f8-2263df30b550 · outbound

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Combining Machine Learning Defenses without Conflicts Papernot et al., ``Semi-supervised knowledge transfer for deep learning from private training data,'' in ICLR, 2017

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Observation fc499850-fcf5-4969-a58c-bd68966a2d33 · outbound

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Combining Machine Learning Defenses without Conflicts Jayaraman and D

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Observation 96d8f0cf-7e7f-42b0-92ea-141a4d130d0a · outbound

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Observation affbd966-cf30-4e46-a0b6-43e63e6131bc · outbound

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Observation b6b122e4-366a-42b4-9d15-fae8811279fc · outbound

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Observation 98192fda-d480-4839-aecc-6d809dd5559f · outbound

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Observation 621fff55-3665-4be4-8f81-9ab0d870ea48 · outbound

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Observation 56f085c9-12e0-4401-ac87-a51d570f22f8 · outbound

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Observation a7ea1405-26ca-498b-9818-42f9ac1bba78 · outbound

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Observation c2d8e682-f777-4071-a237-8b45d49112f9 · outbound

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Combining Machine Learning Defenses without Conflicts 120--129

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Observation 25a8684e-913e-48e7-9849-9dc6139641ed · outbound

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Observation 21e6004a-8bf3-443e-ba39-53b0a391c6ed · outbound

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Observation dbd7c46a-0806-4567-9063-e99cb40832b6 · outbound

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Observation 4403d5b9-a181-44cd-b32c-76b3fe62d943 · outbound

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Observation d2d7c354-617a-44de-9fba-8ff71b3e0388 · outbound

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Combining Machine Learning Defenses without Conflicts Pleiss et al., ``On fairness and calibration,'' in NeurIPS, 2017

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Observation ebdd35cd-5c98-4428-98d9-61a617611dfc · outbound

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Observation 161c3269-f4b0-4a1d-9754-c3715aca1d7e · outbound

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Combining Machine Learning Defenses without Conflicts Unresolved cited work

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Observation 8b0f9a01-497d-417b-8bba-3931543c8214 · outbound

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