Daniel Arp

Signal Processing · 2013–2020

A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 84 citations into it; #1,882 of 7,572 by trust here, #880 by citations
• Artificial Intelligence — 71 citations into it
• Computer Networks and Communications — 63 citations into it; #2,429 of 9,886 by trust here, #1,251 by citations
• Safety, Robustness & Interpretability — 41 citations into it
• Information Systems — 39 citations into it
• Software — 35 citations into it; #268 of 1,227 by trust here, #95 by citations
• Privacy & Security — 17 citations into it; #345 of 709 by trust here, #194 by citations
• Computer Vision and Pattern Recognition — 13 citations into it

Whom Daniel Arp trusts overall

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

1. Yajin Zhou
2. William Enck
3. Battista Biggio
4. Dustin Boswell
5. Vaibhav Rastogi
6. Bhaskar Pratim Sarma
7. Xuxian Jiang
8. Rong-En Fan
9. Yousra Aafer
10. Adrienne Porter Felt

Who trusts Daniel Arp overall

1. Kathrin Grosse
2. Feargus Pendlebury
3. Fabio Pierazzi
4. Davide Maiorca
5. Annamalai Narayanan
6. Arjun Nitin Bhagoji
7. Nicolas Papernot
8. Charles Smutz
9. Ziyun Zhu
10. Andrea Saracino

Papers from Daniel Arp's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet Large Scale Visual Recognition Challenge
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning (2020) — 83 citations
• Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017) — 307 citations
• Comprehensive Analysis and Detection of Flash-Based Malware (2016) — 19 citations
• Modeling and Discovering Vulnerabilities with Code Property Graphs (2014) — 917 citations
• DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014) — 2,526 citations
• Structural detection of android malware using embedded call graphs (2013) — 349 citations