Paper
Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition
Authors
Krzysztof Kotowski, Ramez Shendy, Jakub Nalepa, Agata Kaczmarek, Dawid Płudowski, Piotr Wilczyński, Artur Janicki, Przemysław Biecek, Ambros Marzetta, Atul Pande, Lalit Chandra Routhu, Swapnil Srivastava, Evridiki Ntagiou
Abstract
Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security risk of trojan horse attacks, carried out by hiding a backdoor in the training data or directly in the model weights. Once implanted, the backdoor is activated by a specific trigger pattern at test time, causing the model to produce manipulated predictions. We focus on this issue in our \textit{Trojan Horse Hunt} data science competition, where more than 200 teams faced the task of identifying triggers hidden in deep forecasting models for spacecraft telemetry. We describe the novel task formulation, benchmark set, evaluation protocol, and best solutions from the competition. We further summarize key insights and research directions for effective identification of triggers in time series forecasting models. All materials are publicly available on the official competition webpage https://www.kaggle.com/competitions/trojan-horse-hunt-in-space.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.20108v1</id>\n <title>Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition</title>\n <updated>2026-03-20T16:32:47Z</updated>\n <link href='https://arxiv.org/abs/2603.20108v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.20108v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security risk of trojan horse attacks, carried out by hiding a backdoor in the training data or directly in the model weights. Once implanted, the backdoor is activated by a specific trigger pattern at test time, causing the model to produce manipulated predictions. We focus on this issue in our \\textit{Trojan Horse Hunt} data science competition, where more than 200 teams faced the task of identifying triggers hidden in deep forecasting models for spacecraft telemetry. We describe the novel task formulation, benchmark set, evaluation protocol, and best solutions from the competition. We further summarize key insights and research directions for effective identification of triggers in time series forecasting models. All materials are publicly available on the official competition webpage https://www.kaggle.com/competitions/trojan-horse-hunt-in-space.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CR'/>\n <published>2026-03-20T16:32:47Z</published>\n <arxiv:comment>43 pages, 18 figures</arxiv:comment>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Krzysztof Kotowski</name>\n </author>\n <author>\n <name>Ramez Shendy</name>\n </author>\n <author>\n <name>Jakub Nalepa</name>\n </author>\n <author>\n <name>Agata Kaczmarek</name>\n </author>\n <author>\n <name>Dawid Płudowski</name>\n </author>\n <author>\n <name>Piotr Wilczyński</name>\n </author>\n <author>\n <name>Artur Janicki</name>\n </author>\n <author>\n <name>Przemysław Biecek</name>\n </author>\n <author>\n <name>Ambros Marzetta</name>\n </author>\n <author>\n <name>Atul Pande</name>\n </author>\n <author>\n <name>Lalit Chandra Routhu</name>\n </author>\n <author>\n <name>Swapnil Srivastava</name>\n </author>\n <author>\n <name>Evridiki Ntagiou</name>\n </author>\n </entry>"
}