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Austrian Institute of Technology

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Selected work

Representative Papers

DF2023: The Digital Forensics 2023 Dataset for Image Forgery Detection

Mar 28, 2025

To address the growing threat of maliciously manipulated images on social media platforms for opinion manipulation, this paper introduces DF2023—the first large-scale, open-source benchmark dataset featuring fine-grained annotations across four major image forgery types: splicing, copy-move, enhancement, and object removal. Comprising over one million samples derived from real-world social media propagation scenarios, DF2023 is constructed via multi-source acquisition and rigorous human annotation, enabling comprehensive evaluation of both forgery localization and classification. It establishes the first systematic unification of these four forgery categories, significantly lowering data barriers for algorithm development and facilitating fair, cross-model benchmarking. Third-party reproductions based on DF2023 demonstrate consistent improvements of 12–18% in cross-category generalization performance across multiple detection models. Thus, DF2023 provides a reproducible, highly compatible, and strongly generalizable benchmark for digital image forensics research.

1 citations1 influentialRead paper

AttackMate: Realistic Emulation and Automation of Cyber Attack Scenarios Across the Kill Chain

Jan 20, 2026

This work addresses the limitations of existing adversarial simulation tools, which rely on agent-based instrumentation of target systems, often leaving anomalous artifacts and failing to faithfully replicate human attacker behavior—particularly in critical phases of the cyber kill chain such as initial access and interactive operations. To overcome these shortcomings, the authors propose and implement an open-source attack scripting language coupled with an agentless execution engine that closely emulates real-world attacker tactics. This approach enables high-fidelity, interactive simulation of complete kill chain stages, including initial access, privilege escalation, and lateral movement. Experimental results demonstrate that system logs generated by this method exhibit significantly greater behavioral similarity to those produced by actual human-driven attacks, thereby enhancing the realism and effectiveness of security testing and intrusion detection research.

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