7 papers
Tackle CSM in JPEG Steganalysis with Data Adaptation
Rony Abecidan, Vincent Itier, Jérémie Boulanger +2
Steganalysis models excel on benchmark datasets but struggle in the wild when analyzed images are produced by a processing pipeline unseen during training. This problem known as Co…
Targeted Pooled Latent-Space Steganalysis Applied to Generative Steganography, with a Fix
Etienne Levecque, Aurélien Noirault, Tomáš Pevn{ý} +3
Steganographic schemes dedicated to generated images modify the seed vector in the latent space to embed a message. Whereas most steganalysis methods attempt to detect the embeddin…
Intersectional Fairness via Mixed-Integer Optimization
JiÅÃ NÄmeÄek, Mark Kozdoba, Illia Kryvoviaz +2
The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and transparent. While regulatory frameworks,…
Distillation of a tractable model from the VQ-VAE
Armin HadžiÄ, Milan Papez, Tomáš Pevný
Deep generative models with discrete latent space, such as the Vector-Quantized Variational Autoencoder (VQ-VAE), offer excellent data generation capabilities, but, due to the larg…
Sparse Probabilistic Graph Circuits
Martin Rektoris, Milan Papež, Václav Å mÃdl +1
Deep generative models (DGMs) for graphs achieve impressively high expressive power thanks to very efficient and scalable neural networks. However, these networks contain non-linea…
Generating Likely Counterfactuals Using Sum-Product Networks
Jiri Nemecek, Tomas Pevny, Jakub Marecek
The need to explain decisions made by AI systems is driven by both recent regulation and user demand. The decisions are often explainable only post hoc. In counterfactual explanati…