Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment
(CCS) Model inversion approach in the adversary setting based on training an inversion model that acts as aninverse of the original model. With no fullknowledge about the original training data, an accurate inversion is still possible by training the inversion model on…
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- 2026-10-10
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Adversarial Learning dl.acm.org
(KDD) Reverse engineering of remote linear classifiers, using membership queries.
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Improved Membership Inference Attacks Against Language Classification Models arxiv.org
(ICLR) Presents a framework for running membership inference attacks against classifier, in audit mode.
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Data driven exploratory attacks on black box classifiers in adversarial domains sciencedirect.com
(Neurocomputing) Reverse engineers remote classifier models (e.g., for evading a CAPTCHA test).
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Adversarial Learning adversariallearning.com
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Towards Reverse-Engineering Black-Box Neural Networks arxiv.org
(ICLR) (Code) Infer inner hyperparameters (eg number of layers, non-linear activation type) of a remote neural network model by analysing its response patterns to certain inputs.
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Universal and Transferable Adversarial Attacks on Aligned Language Models arxiv.org
This paper introduces a simple and efficient attack method that enables aligned language models to generate objectionable content with high probability, highlighting the need for improved prevention techniques in large language models. The generated adversarial prompts are…
Audit Algorithms › Papers > 2019: “(CCS) Model inversion approach in the adversary setting based on training an inversion model that acts as aninverse of the original model. With no fullknowledge about the original training data, an accurate inversion is still possible by training the inversion model on…”