Backdoor Channels Hidden in Latent Space: Extending Cryptographic Undetectability to Modern Neural Networks

arXiv:2605.13214 · cs.CR, cs.LG · Submitted 2026-05-13 · Read on arXiv

cs.CR, cs.LG

Submitted: 2026-05-13

Updated: 2026-09-06

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 28× in this paper · explained in 1 episode
• Pruning — 22× in this paper · explained in 4 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Latent Space — 5× in this paper · explained in 7 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Sparsity — 3× in this paper · explained in 4 episodes
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• International Conference on Machine Learning and Applications — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Backdoor Attacks and Defenses in Computer Vision Domain: A Survey
• Computational Lower Bounds for Sparse PCA
• Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Planting Undetectable Backdoors in Machine Learning Models
• Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models
• Adam: A Method for Stochastic Optimization
• Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond
• Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

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