Towards Multi-class Blind Steganalyzer for JPEG Images. - Tomás Pevný; Jessica J. Fridrich (IWDW, 2005)

Multi-class blind steganalysis for JPEG images. - Tomás Pevný; Jessica J. Fridrich (Security, Steganography, and Watermarking of Multimedia Contents, 2006)

Statistically undetectable jpeg steganography: dead ends challenges, and opportunities. - Jessica J. Fridrich; Tomás Pevný; Jan Kodovský (MM&Sec, 2007)

Merging Markov and DCT features for multi-class JPEG steganalysis. - Tomás Pevný; Jessica J. Fridrich (Security, Steganography, and Watermarking of Multimedia Contents, 2007)

Benchmarking for Steganography. - Tomás Pevný; Jessica J. Fridrich (Information Hiding, 2008)

The square root law of steganographic capacity. - Andrew D. Ker; Tomás Pevný; Jan Kodovský; Jessica J. Fridrich (MM&Sec, 2008)

Novelty detection in blind steganalysis. - Tomás Pevný; Jessica J. Fridrich (MM&Sec, 2008)

Estimation of primary quantization matrix for steganalysis of double-compressed JPEG images. - Tomás Pevný; Jessica J. Fridrich (Security, Forensics, Steganography, and Watermarking of Multimedia Contents, 2008)

Detection of Double-Compression in JPEG Images for Applications in Steganography. - Tomás Pevný; Jessica J. Fridrich (IEEE Trans. Inf. Forensics Secur., 2008)

Multiclass Detector of Current Steganographic Methods for JPEG Format. - Tomás Pevný; Jessica J. Fridrich (IEEE Trans. Inf. Forensics Secur., 2008)

Using High-Dimensional Image Models to Perform Highly Undetectable Steganography. - Tomás Pevný; Tomás Filler; Patrick Bas (Information Hiding, 2010)

Modern steganalysis can detect YASS. - Jan Kodovský; Tomás Pevný; Jessica J. Fridrich (Media Forensics and Security, 2010)

Steganalysis by subtractive pixel adjacency matrix. - Tomás Pevný; Patrick Bas; Jessica J. Fridrich (IEEE Trans. Inf. Forensics Secur., 2010)

"Break Our Steganographic System": The Ins and Outs of Organizing BOSS. - Patrick Bas; Tomás Filler; Tomás Pevný (Information Hiding, 2011)

A new paradigm for steganalysis via clustering. - Andrew D. Ker; Tomás Pevný (Media Forensics and Security, 2011)

Detecting messages of unknown length. - Tomás Pevný (Media Forensics and Security, 2011)

Identifying a steganographer in realistic and heterogeneous data sets. - Andrew D. Ker; Tomás Pevný (Media Watermarking, Security, and Forensics, 2012)

Co-occurrence steganalysis in high dimensions. - Tomás Pevný (Media Watermarking, Security, and Forensics, 2012)

Batch steganography in the real world. - Andrew David Ker; Tomás Pevný (MM&Sec, 2012)

Detecting anomalous network hosts by means of PCA. - Tomás Pevný; Martin Rehák; Martin Grill (WIFS, 2012)

From Blind to Quantitative Steganalysis. - Tomás Pevný; Jessica J. Fridrich; Andrew D. Ker (IEEE Trans. Inf. Forensics Secur., 2012)

Attacking the IDS learning processes. - Tomás Pevný; Martin Komon; Martin Rehák (ICASSP, 2013)

Moving steganography and steganalysis from the laboratory into the real world. - Andrew D. Ker; Patrick Bas; Rainer Böhme; Rémi Cogranne; Scott Craver; Tomás Filler; Jessica J. Fridrich; Tomás Pevný (IH&MMSec, 2013)

The challenges of rich features in universal steganalysis. - Tomás Pevný; Andrew D. Ker (Media Watermarking, Security, and Forensics, 2013)

Steganographic key leakage through payload metadata. - Tomás Pevný; Andrew D. Ker (IH&MMSec, 2014)

A mishmash of methods for mitigating the model mismatch mess. - Andrew D. Ker; Tomás Pevný (Media Watermarking, Security, and Forensics, 2014)

Randomized Operating Point Selection in Adversarial Classification. - Viliam Lisý; Robert Kessl; Tomás Pevný (ECML/PKDD (2), 2014)

The Steganographer is the Outlier: Realistic Large-Scale Steganalysis. - Andrew D. Ker; Tomás Pevný (IEEE Trans. Inf. Forensics Secur., 2014)

Unsupervised detection of malware in persistent web traffic. - Jan Kohout; Tomás Pevný (ICASSP, 2015)

Automatic discovery of web servers hosting similar applications. - Jan Kohout; Tomás Pevný (IM, 2015)

Towards dependable steganalysis. - Tomás Pevný; Andrew D. Ker (Media Watermarking, Security, and Forensics, 2015)

Is ensemble classifier needed for steganalysis in high-dimensional feature spaces? - Rémi Cogranne; Vahid Sedighi; Jessica J. Fridrich; Tomás Pevný (WIFS, 2015)

Optimizing pooling function for pooled steganalysis. - Tomás Pevný; Ivan Nikolaev (WIFS, 2015)

Discriminative Models for Multi-instance Problems with Tree Structure. - Tomás Pevný; Petr Somol (AISec@CCS, 2016)

Rethinking Optimal Embedding. - Andrew D. Ker; Tomás Pevný; Patrick Bas (IH&MMSec, 2016)

Malicons: Detecting Payload in Favicons. - Tomás Pevný; Martin Kopp; Jakub Kroustek; Andrew D. Ker (Media Watermarking, Security, and Forensics, 2016)

k-NN Classification of Malware in HTTPS Traffic Using the Metric Space Approach. - Jakub Lokoc; Jan Kohout; Premysl Cech; Tomás Skopal; Tomás Pevný (PAISI, 2016)

Feature Extraction and Malware Detection on Large HTTPS Data Using MapReduce. - Premysl Cech; Jan Kohout; Jakub Lokoc; Tomás Komárek; Jakub Marousek; Tomás Pevný (SISAP, 2016)

Passive NAT detection using HTTP access logs. - Tomás Komárek; Martin Grill; Tomás Pevný (WIFS, 2016)

Learning combination of anomaly detectors for security domain. - Martin Grill; Tomás Pevný (Comput. Networks, 2016)

Using Behavioral Similarity for Botnet Command-and-Control Discovery. - Jan Jusko; Martin Rehák; Jan Stiborek; Jan Kohout; Tomás Pevný (IEEE Intell. Syst., 2016)

Loda: Lightweight on-line detector of anomalies. - Tomás Pevný (Mach. Learn., 2016)

Optimal Strategies for Detecting Data Exfiltration by Internal and External Attackers. - Karel Durkota; Viliam Lisý; Christopher Kiekintveld; Karel Horák 002; Branislav Bosanský; Tomás Pevný (GameSec, 2017)

Using Neural Network Formalism to Solve Multiple-Instance Problems. - Tomás Pevný; Petr Somol (ISNN (1), 2017)

Malware Detection by Analysing Encrypted Network Traffic with Neural Networks. - Paul Prasse; Lukás Machlica; Tomás Pevný; Jirí Havelka; Tobias Scheffer (ECML/PKDD (2), 2017)

Malware Discovery Using Behaviour-Based Exploration of Network Traffic. - Jakub Lokoc; Tomás Grosup; Premysl Cech; Tomás Pevný; Tomás Skopal (SISAP, 2017)

Malware Detection by Analysing Network Traffic with Neural Networks. - Paul Prasse; Lukás Machlica; Tomás Pevný; Jirí Havelka; Tobias Scheffer (IEEE Symposium on Security and Privacy Workshops, 2017)

Reducing false positives of network anomaly detection by local adaptive multivariate smoothing. - Martin Grill; Tomás Pevný; Martin Rehák (J. Comput. Syst. Sci., 2017)

Exploring Non-Additive Distortion in Steganography. - Tomás Pevný; Andrew D. Ker (IH&MMSec, 2018)

Probabilistic analysis of dynamic malware traces. - Jan Stiborek; Tomás Pevný; Martin Rehák (Comput. Secur., 2018)

Algorithms for solving optimization problems arising from deep neural net models: smooth problems. - Vyacheslav Kungurtsev; Tomás Pevný (CoRR, 2018)

Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems. - Vyacheslav Kungurtsev; Tomás Pevný (CoRR, 2018)

Are generative deep models for novelty detection truly better? - Vít Skvára; Tomás Pevný; Václav Smídl (CoRR, 2018)

Multiple instance learning for malware classification. - Jan Stiborek; Tomás Pevný; Martin Rehák (Expert Syst. Appl., 2018)

Network Traffic Fingerprinting Based on Approximated Kernel Two-Sample Test. - Jan Kohout; Tomás Pevný (IEEE Trans. Inf. Forensics Secur., 2018)

Classification with Costly Features Using Deep Reinforcement Learning. - Jaromír Janisch; Tomás Pevný; Viliam Lisý (AAAI, 2019)

Exploiting Adversarial Embeddings for Better Steganography. - Solène Bernard; Tomás Pevný; Patrick Bas; John Klein (IH&MMSec, 2019)

Anomaly scores for generative models. - Václav Smídl; Jan Bím; Tomás Pevný (CoRR, 2019)

Approximation capability of neural networks on spaces of probability measures and tree-structured domains. - Tomás Pevný; Vojtech Kovarík (CoRR, 2019)

Deep Reinforcement Learning with Explicitly Represented Knowledge and Variable State and Action Spaces. - Jaromír Janisch; Tomás Pevný; Viliam Lisý (CoRR, 2019)

Rodent: Relevance determination in ODE. - Niklas Heim; Václav Smídl; Tomás Pevný (CoRR, 2019)

Joint detection of malicious domains and infected clients. - Paul Prasse; René Knaebel; Lukás Machlica; Tomás Pevný; Tobias Scheffer (Mach. Learn., 2019)

Loss Functions for Clustering in Multi-instance Learning. - Marek Dedic; Tomás Pevný; Lukás Bajer; Martin Holena (ITAT, 2020)

Neural Power Units. - Niklas Heim; Tomás Pevný; Václav Smídl (NeurIPS, 2020)

Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations. - Tomás Pevný; Václav Smídl; Martin Trapp; Ondrej Polácek; Tomás Oberhuber (PGM, 2020)

Nested Multiple Instance Learning in Modelling of HTTP network traffic. - Tomás Pevný; Marek Dedic (CoRR, 2020)

Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks. - Jaromír Janisch; Tomás Pevný; Viliam Lisý (CoRR, 2020)

Anomaly explanation with random forests. - Martin Kopp; Tomás Pevný; Martin Holena (Expert Syst. Appl., 2020)

Classification with costly features as a sequential decision-making problem. - Jaromír Janisch; Tomás Pevný; Viliam Lisý (Mach. Learn., 2020)

When Should You Defend Your Classifier? - - A Game-Theoretical Analysis of Countermeasures Against Adversarial Examples. - Maximilian Samsinger; Florian Merkle; Pascal Schöttle; Tomás Pevný (GameSec, 2021)

Optimizing Additive Approximations of Non-additive Distortion Functions. - Solène Bernard; Patrick Bas; Tomás Pevný; John Klein (IH&MMSec, 2021)

Mapping the Internet: Modelling Entity Interactions in Complex Heterogeneous Networks. - Simon Mandlík; Tomás Pevný (CoRR, 2021)

Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data. - Simon Mandlík; Matej Racinsky; Viliam Lisý; Tomás Pevný (CoRR, 2021)

Fitting large mixture models using stochastic component selection. - Milan Papez; Tomás Pevný; Václav Smídl (CoRR, 2021)

Heuristic Search Planning with Deep Neural Networks using Imitation, Attention and Curriculum Learning. - Leah Chrestien; Tomás Pevný; Antonín Komenda; Stefan Edelkamp (CoRR, 2021)

Explicit Optimization of min max Steganographic Game. - Solène Bernard; Patrick Bas; John Klein; Tomás Pevný (IEEE Trans. Inf. Forensics Secur., 2021)

Using Set Covering to Generate Databases for Holistic Steganalysis. - Rony Abecidan; Vincent Itier; Jérémie Boulanger; Patrick Bas; Tomás Pevný (WIFS, 2022)

Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data. - Tomás Pevný; Viliam Lisý; Branislav Bosanský; Petr Somol; Michal Pechoucek (CoRR, 2022)

A Differentiable Loss Function for Learning Heuristics in A. - Leah Chrestien; Tomás Pevný; Antonín Komenda; Stefan Edelkamp (CoRR, 2022)

JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data. - Simon Mandlík; Matej Racinsky; Viliam Lisý; Tomás Pevný (J. Mach. Learn. Res., 2022)

General framework for binary classification on top samples. - Lukás Adam; Václav Mácha 001; Václav Smídl; Tomás Pevný (Optim. Methods Softw., 2022)

Backpack: A Backpropagable Adversarial Embedding Scheme. - Solène Bernard; Patrick Bas; John Klein; Tomás Pevný (IEEE Trans. Inf. Forensics Secur., 2022)

Comparison of Anomaly Detectors: Context Matters. - Vít Skvára; Jan Francu; Matej Zorek; Tomás Pevný; Václav Smídl (IEEE Trans. Neural Networks Learn. Syst., 2022)

Heuristic Search Optimisation Using Planning and Curriculum Learning Techniques. - Leah Chrestien; Tomás Pevný; Stefan Edelkamp; Antonín Komenda (EPIA (1), 2023)

NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios. - Jaromír Janisch; Tomás Pevný; Viliam Lisý (ESORICS Workshops (2), 2023)

Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal. - Leah Chrestien; Stefan Edelkamp; Antonín Komenda; Tomás Pevný (NeurIPS, 2023)

Leveraging Data Geometry to Mitigate CSM in Steganalysis. - Rony Abecidan; Vincent Itier; Jérémie Boulanger; Patrick Bas; Tomás Pevný (WIFS, 2023)

Is AUC the best measure for practical comparison of anomaly detectors? - Vít Skvára; Tomás Pevný; Václav Smídl (CoRR, 2023)

Improving the Validity of Decision Trees as Explanations. - Jiri Nemecek 002; Tomás Pevný; Jakub Marecek (CoRR, 2023)

The Non-Zero-Sum Game of Steganography in Heterogeneous Environments. - Quentin Giboulot; Tomás Pevný; Andrew D. Ker (IEEE Trans. Inf. Forensics Secur., 2023)

Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs. - Milan Papez; Martin Rektoris; Václav Smídl; Tomás Pevný (ICLR, 2024)

Blind Data Adaptation to tackle Covariate Shift in Operational Steganalysis. - Rony Abecidan; Vincent Itier; Jérémie Boulanger; Patrick Bas; Tomás Pevný (CoRR, 2024)

GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings. - Milan Papez; Martin Rektoris; Václav Smídl; Tomás Pevný (CoRR, 2024)

Deep anomaly detection on set data: Survey and comparison. - Michaela Masková; Matej Zorek; Tomás Pevný; Václav Smídl (Pattern Recognit., 2024)

Malicious Internet Entity Detection Using Local Graph Inference. - Simon Mandlík; Tomás Pevný; Václav Smídl; Lukás Bajer (IEEE Trans. Inf. Forensics Secur., 2024)

On the Economics of Adversarial Machine Learning. - Florian Merkle; Maximilian Samsinger; Pascal Schöttle; Tomás Pevný (IEEE Trans. Inf. Forensics Secur., 2024)

Classification with costly features in hierarchical deep sets. - Jaromír Janisch; Tomás Pevný; Viliam Lisý (Mach. Learn., 2024)

Anomaly detection in multifactor data. - Vít Skvára; Václav Smídl; Tomás Pevný (Neural Comput. Appl., 2024)

State Encodings for GNN-Based Lifted Planners. - Rostislav Horcík; Gustav Sír; Vítezslav Simek; Tomás Pevný (AAAI, 2025)

Generating Likely Counterfactuals Using Sum-Product Networks. - Jiri Nemecek 002; Tomás Pevný; Jakub Marecek (ICLR, 2025)

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs. - Milan Papez; Martin Rektoris; Václav Smídl; Tomás Pevný (UAI, 2025)

Bias Detection via Maximum Subgroup Discrepancy. - Jiri Nemecek 002; Mark Kozdoba; Illia Kryvoviaz; Tomás Pevný; Jakub Marecek (CoRR, 2025)

Sparse Probabilistic Graph Circuits. - Martin Rektoris; Milan Papez; Václav Smídl; Tomás Pevný (CoRR, 2025)