Michael Backes

Artificial Intelligence · 2011–2023

A researcher in the Science corpus: 21 papers; trusts 289 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 504 citations into it; #3,995 of 35,345 by trust here, #2,301 by citations
Safety, Robustness & Interpretability — 278 citations into it; #569 of 2,251 by trust here, #372 by citations
Signal Processing — 122 citations into it; #1,384 of 7,572 by trust here, #386 by citations
Privacy & Security — 96 citations into it
Computer Vision and Pattern Recognition — 93 citations into it; #15,702 of 21,575 by trust here, #10,677 by citations
Information Systems — 76 citations into it
Natural Language Processing — 64 citations into it
Computer Networks and Communications — 61 citations into it; #2,290 of 9,886 by trust here, #690 by citations

Whom Michael Backes trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Michael Backes overall

Papers from Michael Backes's perspective

Ordered by how strongly the researchers Michael Backes trusts endorse each paper. Same corpus, a different order for every researcher.

Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
ImageNet classification with deep convolutional neural networks
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Long Short-Term Memory
Gradient-based learning applied to document recognition
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

Adversarial Perturbations Against Deep Neural Networks for Malware Classification (2016) — 451 citations
On the (Statistical) Detection of Adversarial Examples (2017) — 768 citations
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models (2018) — 1,184 citations
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (2019) — 475 citations
Adversarial Examples for Malware Detection (2017) — 589 citations
Membership Privacy in MicroRNA-based Studies (2016) — 155 citations
Dynamic Backdoor Attacks Against Machine Learning Models (2020) — 335 citations
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models (2023) — 675 citations
Why So Toxic?: Measuring and Triggering Toxic Behavior in Open-Domain Chatbots (2022) — 86 citations
On the security relevance of weights in deep learning. (2020)
When Machine Unlearning Jeopardizes Privacy (2020) — 325 citations
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements (2020) — 363 citations
Decentralized Privacy-Preserving Proximity Tracing (2020) — 366 citations
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (2019) — 298 citations
Adversarial Initialization - when your network performs the way I want (2019) — 8 citations
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service (2018) — 124 citations
Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security (2017) — 300 citations
You Get Where You're Looking for: The Impact of Information Sources on Code Security (2016) — 333 citations
Reliable Third-Party Library Detection in Android and its Security Applications (2016) — 353 citations
ADSNARK: Nearly Practical and Privacy-Preserving Proofs on Authenticated Data (2015) — 109 citations