Guilherme Perin

1.2k total citations
28 papers, 417 citations indexed

About

Guilherme Perin is a scholar working on Artificial Intelligence, Signal Processing and Hardware and Architecture. According to data from OpenAlex, Guilherme Perin has authored 28 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 10 papers in Signal Processing and 10 papers in Hardware and Architecture. Recurrent topics in Guilherme Perin's work include Cryptographic Implementations and Security (23 papers), Advanced Malware Detection Techniques (10 papers) and Physical Unclonable Functions (PUFs) and Hardware Security (9 papers). Guilherme Perin is often cited by papers focused on Cryptographic Implementations and Security (23 papers), Advanced Malware Detection Techniques (10 papers) and Physical Unclonable Functions (PUFs) and Hardware Security (9 papers). Guilherme Perin collaborates with scholars based in Netherlands, France and Germany. Guilherme Perin's co-authors include Stjepan Picek, Lichao Wu, Łukasz Chmielewski, Lejla Batina, Luca Mariot, Lionel Torres, Philippe Maurine, Laurent Imbert, Shivam Bhasin and Huimin Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, ACM Computing Surveys and IEEE Transactions on Information Forensics and Security.

In The Last Decade

Guilherme Perin

26 papers receiving 405 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Guilherme Perin Netherlands 12 339 178 158 136 55 28 417
Annelie Heuser France 13 537 1.6× 324 1.8× 257 1.6× 230 1.7× 78 1.4× 27 628
Louis Wingers United States 2 330 1.0× 118 0.7× 68 0.4× 250 1.8× 38 0.7× 2 413
Seokhie Hong South Korea 13 463 1.4× 125 0.7× 74 0.5× 261 1.9× 65 1.2× 122 569
Leif Uhsadel Belgium 7 361 1.1× 96 0.5× 72 0.5× 196 1.4× 57 1.0× 9 474
D. Hwang United States 10 250 0.7× 214 1.2× 81 0.5× 125 0.9× 68 1.2× 21 382
Kaan Kara Switzerland 9 165 0.5× 153 0.9× 26 0.2× 90 0.7× 47 0.9× 27 377
R.B. Lee United States 12 306 0.9× 138 0.8× 128 0.8× 100 0.7× 78 1.4× 23 418
A.J. Elbirt United States 11 440 1.3× 145 0.8× 47 0.3× 343 2.5× 47 0.9× 19 576
Ahmet Can Mert Türkiye 14 351 1.0× 80 0.4× 158 1.0× 202 1.5× 61 1.1× 30 615
Daniel Page United Kingdom 13 378 1.1× 165 0.9× 76 0.5× 105 0.8× 68 1.2× 55 493

Countries citing papers authored by Guilherme Perin

Since Specialization
Citations

This map shows the geographic impact of Guilherme Perin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Guilherme Perin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guilherme Perin more than expected).

Fields of papers citing papers by Guilherme Perin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guilherme Perin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Guilherme Perin. The network helps show where Guilherme Perin may publish in the future.

Co-authorship network of co-authors of Guilherme Perin

This figure shows the co-authorship network connecting the top 25 collaborators of Guilherme Perin. A scholar is included among the top collaborators of Guilherme Perin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Guilherme Perin. Guilherme Perin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wu, Lichao, Guilherme Perin, & Stjepan Picek. (2024). Weakly Profiling Side-channel Analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems. 2024(3). 707–730. 1 indexed citations
2.
Wu, Lichao, et al.. (2024). Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis. Mathematics. 12(20). 3279–3279. 1 indexed citations
3.
Wu, Lichao, et al.. (2024). Leakage Model-flexible Deep Learning-based Side-channel Analysis. Bristol Research (University of Bristol). 3 indexed citations
4.
Wu, Lichao, et al.. (2024). Plaintext-based Side-channel Collision Attack. Bristol Research (University of Bristol).
5.
Wu, Lichao, et al.. (2023). Label Correlation in Deep Learning-Based Side-Channel Analysis. IEEE Transactions on Information Forensics and Security. 18. 3849–3861. 7 indexed citations
6.
Perin, Guilherme, et al.. (2023). Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis. Journal of Cryptographic Engineering. 14(3). 475–497. 1 indexed citations
7.
Wu, Lichao, et al.. (2023). Ablation Analysis for Multi-Device Deep Learning-Based Physical Side-Channel Analysis. IEEE Transactions on Dependable and Secure Computing. 21(3). 1331–1341. 6 indexed citations
8.
Perin, Guilherme, Lichao Wu, & Stjepan Picek. (2023). The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA. Algorithms. 16(3). 127–127. 2 indexed citations
9.
Wu, Lichao, et al.. (2023). No (good) loss no gain: systematic evaluation of loss functions in deep learning-based side-channel analysis. Journal of Cryptographic Engineering. 13(3). 311–324. 12 indexed citations
10.
Perin, Guilherme, et al.. (2023). Resolving the Doubts: On the Construction and Use of ResNets for Side-Channel Analysis. Mathematics. 11(15). 3265–3265. 3 indexed citations
11.
Wu, Lichao, Guilherme Perin, & Stjepan Picek. (2022). I Choose You: Automated Hyperparameter Tuning for Deep Learning-Based Side-Channel Analysis. IEEE Transactions on Emerging Topics in Computing. 12(2). 546–557. 56 indexed citations
12.
Picek, Stjepan, Guilherme Perin, Luca Mariot, Lichao Wu, & Lejla Batina. (2022). SoK: Deep Learning-based Physical Side-channel Analysis. ACM Computing Surveys. 55(11). 1–35. 57 indexed citations
13.
Wu, Lichao, Guilherme Perin, & Stjepan Picek. (2022). The Best of Two Worlds: Deep Learning-assisted Template Attack. IACR Transactions on Cryptographic Hardware and Embedded Systems. 413–437. 27 indexed citations
14.
Wu, Lichao, et al.. (2021). Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis. IACR Transactions on Cryptographic Hardware and Embedded Systems. 677–707. 74 indexed citations
15.
Perin, Guilherme, Łukasz Chmielewski, Lejla Batina, & Stjepan Picek. (2020). Keep it Unsupervised: Horizontal Attacks Meet Deep Learning. IACR Transactions on Cryptographic Hardware and Embedded Systems. 343–372. 25 indexed citations
16.
Perin, Guilherme, Laurent Imbert, Philippe Maurine, & Lionel Torres. (2015). Vertical and horizontal correlation attacks on RNS-based exponentiations. Journal of Cryptographic Engineering. 5(3). 171–185. 2 indexed citations
17.
Perin, Guilherme, Lionel Torres, Pascal Benoit, & Philippe Maurine. (2012). Amplitude demodulation-based EM analysis of different RSA implementations. Design, Automation, and Test in Europe. 1167–1172. 1 indexed citations
18.
Perin, Guilherme, et al.. (2011). Montgomery Modular Multiplication on Reconfigurable Hardware: Systolic versus Multiplexed Implementation. SHILAP Revista de lepidopterología. 2011. 1–10. 15 indexed citations
19.
Perin, Guilherme, et al.. (2009). A Gigabit UDP/IP network stack in FPGA. 836–839. 13 indexed citations
20.
Perin, Guilherme. (2008). O Povo no horário eleitoral - sobre a construção desta categoria nas campanhas de Lula e Alckmin. Lume (Universidade Federal do Rio Grande do Sul).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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