Kassem Kallas

526 total citations
19 papers, 264 citations indexed

About

Kassem Kallas is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kassem Kallas has authored 19 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 9 papers in Computer Networks and Communications and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kassem Kallas's work include Adversarial Robustness in Machine Learning (8 papers), Distributed Sensor Networks and Detection Algorithms (7 papers) and Target Tracking and Data Fusion in Sensor Networks (5 papers). Kassem Kallas is often cited by papers focused on Adversarial Robustness in Machine Learning (8 papers), Distributed Sensor Networks and Detection Algorithms (7 papers) and Target Tracking and Data Fusion in Sensor Networks (5 papers). Kassem Kallas collaborates with scholars based in France, Italy and United States. Kassem Kallas's co-authors include Mauro Barni, Benedetta Tondi, Andrea Abrardo, Teddy Furon, Ehsan Nowroozi, Thao T. Nguyen, Michael R. Souryal, Abhir Bhalerao, Alireza Jolfaei and Yannick Teglia and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Information Fusion.

In The Last Decade

Kassem Kallas

17 papers receiving 257 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kassem Kallas France 7 190 86 77 70 32 19 264
Aleksandra Mileva North Macedonia 8 136 0.7× 88 1.0× 107 1.4× 67 1.0× 40 1.3× 32 256
Wafaa Mustafa Abduallah Iraq 7 115 0.6× 94 1.1× 119 1.5× 70 1.0× 16 0.5× 10 241
Khalid Chougdali Morocco 7 100 0.5× 56 0.7× 106 1.4× 66 0.9× 9 0.3× 59 198
Hua Wu China 9 174 0.9× 48 0.6× 174 2.3× 70 1.0× 19 0.6× 62 289
Jon Sneyers Belgium 9 128 0.7× 171 2.0× 69 0.9× 55 0.8× 12 0.4× 30 310
Xueluan Gong China 13 326 1.7× 62 0.7× 124 1.6× 92 1.3× 31 1.0× 30 407
Yiting Liu China 8 171 0.9× 67 0.8× 122 1.6× 52 0.7× 8 0.3× 29 243
Kalikinkar Mandal Canada 9 132 0.7× 57 0.7× 34 0.4× 16 0.2× 65 2.0× 23 183
Bao Gia Doan Australia 5 281 1.5× 73 0.8× 65 0.8× 112 1.6× 21 0.7× 8 316

Countries citing papers authored by Kassem Kallas

Since Specialization
Citations

This map shows the geographic impact of Kassem Kallas'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 Kassem Kallas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kassem Kallas more than expected).

Fields of papers citing papers by Kassem Kallas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kassem Kallas. 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 Kassem Kallas. The network helps show where Kassem Kallas may publish in the future.

Co-authorship network of co-authors of Kassem Kallas

This figure shows the co-authorship network connecting the top 25 collaborators of Kassem Kallas. A scholar is included among the top collaborators of Kassem Kallas 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 Kassem Kallas. Kassem Kallas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Coatrieux, Gouenou, et al.. (2025). RoSe-Mix: Robust and Secure Deep Neural Network Watermarking in Black-Box Settings via Image Mixup. Machine Learning and Knowledge Extraction. 7(2). 32–32. 1 indexed citations
2.
Kallas, Kassem, et al.. (2024). Strategic safeguarding: A game theoretic approach for analyzing attacker-defender behavior in DNN backdoors. SHILAP Revista de lepidopterología. 2024(1).
3.
Kallas, Kassem, et al.. (2024). A Double-Edged Sword: The Power of Two in Defending Against DNN Backdoor Attacks. SPIRE - Sciences Po Institutional REpository. 2007–2011.
4.
Nowroozi, Ehsan, Kassem Kallas, & Alireza Jolfaei. (2024). Adversarial Multimedia Forensics. SPIRE - Sciences Po Institutional REpository. 4 indexed citations
5.
Teglia, Yannick, et al.. (2024). A Comprehensive Survey on Backdoor Attacks and Their Defenses in Face Recognition Systems. IEEE Access. 12. 47433–47468. 5 indexed citations
6.
Kallas, Kassem, et al.. (2024). REStore: Exploring a Black-Box Defense against DNN Backdoors using Rare Event Simulation. SPIRE - Sciences Po Institutional REpository. 286–308. 2 indexed citations
7.
Kallas, Kassem & Teddy Furon. (2023). Mixer: DNN Watermarking using Image Mixup. SPIRE - Sciences Po Institutional REpository. 1–5. 4 indexed citations
8.
Nguyen, Thao T., et al.. (2022). Deep Learning for Path Loss Prediction in the 3.5 GHz CBRS Spectrum Band. 2022 IEEE Wireless Communications and Networking Conference (WCNC). 1665–1670. 12 indexed citations
9.
Kallas, Kassem & Teddy Furon. (2022). ROSE: A RObust and SEcure DNN Watermarking. SPIRE - Sciences Po Institutional REpository. 1–6. 9 indexed citations
10.
Kallas, Kassem, et al.. (2021). Deep Learning for Radar Signal Detection in the 3.5 GHz CBRS Band. SPIRE - Sciences Po Institutional REpository. 1–8. 8 indexed citations
11.
Abrardo, Andrea, Mauro Barni, Kassem Kallas, & Benedetta Tondi. (2020). Information Fusion in Distributed Sensor Networks with Byzantines. Signals and communication technology. 5 indexed citations
12.
Barni, Mauro, Kassem Kallas, & Benedetta Tondi. (2019). A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning. Use Siena air (University of Siena). 154 indexed citations
13.
Barni, Mauro, Kassem Kallas, Ehsan Nowroozi, & Benedetta Tondi. (2019). On the Transferability of Adversarial Examples against CNN-based Image Forensics. Use Siena air (University of Siena). 16 indexed citations
14.
Bhalerao, Abhir, Kassem Kallas, Benedetta Tondi, & Mauro Barni. (2019). Luminance-based video backdoor attack against anti-spoofing rebroadcast detection. Warwick Research Archive Portal (University of Warwick). 1–6. 11 indexed citations
15.
Abrardo, Andrea, Mauro Barni, Kassem Kallas, & Benedetta Tondi. (2018). Decision Fusion with Unbalanced Priors under Synchronized Byzantine Attacks: a Message-Passing Approach. SPIRE - Sciences Po Institutional REpository. 1160–1167. 3 indexed citations
16.
Abrardo, Andrea, Mauro Barni, Kassem Kallas, & Benedetta Tondi. (2017). A message passing approach for decision fusion in adversarial multi-sensor networks. Information Fusion. 40. 101–111. 4 indexed citations
17.
Abrardo, Andrea, Mauro Barni, Kassem Kallas, & Benedetta Tondi. (2017). A Message Passing Approach for Decision Fusion of Hidden-Markov Observations in the presence of Synchronized Attacks. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
18.
Abrardo, Andrea, Mauro Barni, Kassem Kallas, & Benedetta Tondi. (2016). A Game-Theoretic Framework for Optimum Decision Fusion in the Presence of Byzantines. IEEE Transactions on Information Forensics and Security. 11(6). 1333–1345. 19 indexed citations
19.
Abrardo, Andrea, Mauro Barni, Kassem Kallas, & Benedetta Tondi. (2014). Decision fusion with corrupted reports in multi-sensor networks: A game-theoretic approach. SPIRE - Sciences Po Institutional REpository. 505–510. 6 indexed citations

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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