aiqbench analysis
This research analyzes the sensitivity of classical versus deep learning models to bit-level parameter perturbations, revealing that neural networks possess significantly higher steganographic capacity due to their parameter redundancy. The findings demonstrate a fundamental trade-off where classical models like HMMs and SVMs are brittle to specific changes, whereas MLPs and LSTMs can absorb modifications with minimal performance impact. These insights offer critical implications for model robustness against adversarial attacks and the feasibility of embedding hidden information within trained weights.
On the Effect of Bit-Level Parameter Perturbations in Machine Learning and Deep Learning Models
arXiv:2609.26280v1 Announce Type: new Abstract: In this chapter, we investigate how classical machine learning models respond to small, targeted modifications in their parameters. We compare and contrast these results to analogous experiments on deep learning models. For classical learning models, we consider Hidden Markov Models (HMM) and Support Vector Machines (SVM), and for comparison, we conduct analogous experiments involving Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks. When applied to the Drebin Android malware dataset, our results show that classical models are brittle, in the sense that a limited set of selected parameters can have a dramatic effect on model behavior. In a related set of experiments, we investigate the steganographic capacity of these same learning models, that is, the proportion of bits in model parameters that can be overwritten without having a significant adverse affect on a model. We find that classical models offer limited steganographic capacity due to their compact, parameter-efficient, and relatively sensitive parameter structure. In contrast, neural networks are parameter-redundant, enabling higher steganographic capacity, where modifications can be distributed across many parameters with minimal impact on performance. These results highlight differences in how classical and neural models respond to parameter changes, with clear implications for both robustness and hidden information embedding. Overall, this work provides a framework for understanding parameter sensitivity and steganographic capacity across different classes of learning models.
Abstract:In this chapter, we investigate how classical machine learning models respond to small, targeted modifications in their parameters. We compare and contrast these results to analogous experiments on deep learning models. For classical learning models, we consider Hidden Markov Models (HMM) and Support Vector Machines (SVM), and for comparison, we conduct analogous experiments involving Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks. When applied to the Drebin Android malware dataset, our results show that classical models are brittle, in the sense that a limited set of selected parameters can have a dramatic effect on model behavior. In a related set of experiments, we investigate the steganographic capacity of these same learning models, that is, the proportion of bits in model parameters that can be overwritten without having a significant adverse affect on a model. We find that classical models offer limited steganographic capacity due to their compact, parameter-efficient, and relatively sensitive parameter structure. In contrast, neural networks are parameter-redundant, enabling higher steganographic capacity, where modifications can be distributed across many parameters with minimal impact on performance. These results highlight differences in how classical and neural models respond to parameter changes, with clear implications for both robustness and hidden information embedding. Overall, this work provides a framework for understanding parameter sensitivity and steganographic capacity across different classes of learning models.
Machine Learning (cs.LG)
arXiv:2609.26280 [cs.LG]
(or arXiv:2609.26280v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.26280 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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