Beyond Deep Learning: Classic Algorithms Fall to AI’s Hidden Weaknesses

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Author: Denis Avetisyan

New research reveals that even traditional machine learning methods are susceptible to adversarial attacks originating from deep neural networks, debunking the notion that simpler models are inherently secure.

Histograms of Oriented Gradients (HOG)-based classifiers exhibit a notable sensitivity to the magnitude of adversarial perturbations, with a discernible gap emerging between performance under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks-a phenomenon suggesting vulnerabilities disproportionate to perturbation strength.
Histograms of Oriented Gradients (HOG)-based classifiers exhibit a notable sensitivity to the magnitude of adversarial perturbations, with a discernible gap emerging between performance under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks-a phenomenon suggesting vulnerabilities disproportionate to perturbation strength.

Feature engineering offers no robust defense against neural adversarial transfer, demonstrating a fundamental vulnerability that transcends computational paradigms.

Despite growing concerns about the fragility of deep neural networks, it remains unclear whether classical machine learning pipelines-those relying on handcrafted features-inherit this vulnerability when attacked via neural surrogates. This work, ‘Adversarial Vulnerability Transcends Computational Paradigms: Feature Engineering Provides No Defense Against Neural Adversarial Transfer’, comprehensively investigates adversarial transfer from deep neural networks to classifiers employing Histogram of Oriented Gradients (HOG) features. Our results demonstrate that these classical pipelines are surprisingly susceptible to adversarial examples generated from neural networks, experiencing accuracy drops comparable to those observed in neural-to-neural transfer. This challenges the assumption that feature engineering provides inherent robustness and raises critical questions about the security of image classification systems across diverse computational paradigms.

The Fragility of Perception: A System’s Blind Spot

Despite their demonstrated prowess in tasks like image recognition, Deep Neural Networks exhibit a surprising fragility when confronted with adversarial examples. These are carefully crafted inputs – often images – altered by perturbations imperceptible to the human eye, yet capable of inducing misclassification with high confidence. The phenomenon isn’t a matter of simply ‘tricking’ the network; rather, these subtle changes exploit the high-dimensional decision boundaries learned during training, pushing the input across a threshold into an incorrect category. Researchers have demonstrated that even state-of-the-art models can be fooled by adversarial examples generated using relatively simple algorithms, raising serious questions about their robustness and reliability in real-world applications where malicious manipulation is a possibility. This vulnerability isn’t merely a theoretical curiosity; it represents a fundamental weakness in how these networks currently ‘see’ and interpret data.

The susceptibility of deep neural networks to adversarial examples presents a significant challenge to their deployment in applications where accuracy is paramount. Consider autonomous vehicles, where a subtly altered stop sign – imperceptible to a human driver – could be misclassified, potentially leading to a collision. Similarly, in medical diagnosis, a minor perturbation to an image of a tumor could result in a false negative, delaying crucial treatment. These failures aren’t simply errors; they expose a fundamental weakness in how these systems ‘see’ the world – a reliance on surface-level patterns rather than robust, conceptual understanding. The implications extend beyond convenience; the potential for real-world harm necessitates a critical re-evaluation of the reliability and safety of these increasingly prevalent technologies.

Deep Neural Networks, despite achieving impressive accuracy on many tasks, often rely on identifying statistical correlations within training data rather than developing a robust, conceptual understanding of the information itself. This reliance creates a fundamental fragility; the networks excel at recognizing patterns similar to those encountered during training, but struggle with even minor deviations. Consequently, these systems are easily fooled by adversarial examples – carefully crafted inputs designed to exploit this dependence on superficial features. Instead of ‘seeing’ an object, the network essentially ‘guesses’ based on probabilistic associations, making it vulnerable to inputs that statistically resemble something else, even if visually distinct to a human observer. This highlights a critical limitation: high performance does not necessarily equate to genuine intelligence or reliable generalization beyond the training distribution.

HOG-based classifiers demonstrate increased sensitivity to adversarial perturbations as the perturbation magnitude increases from <span class="katex-eq" data-katex-display="false">\epsilon = 4/255</span> to <span class="katex-eq" data-katex-display="false">\epsilon = 8/255</span>.
HOG-based classifiers demonstrate increased sensitivity to adversarial perturbations as the perturbation magnitude increases from \epsilon = 4/255 to \epsilon = 8/255.

The Echo of Weakness: Transferability as a Symptom

Adversarial examples exhibit transferability, meaning that perturbations crafted to deceive one machine learning model frequently result in misclassification by other, independently trained models. This phenomenon indicates the vulnerabilities exploited by adversarial attacks are not solely attributable to specific model architectures, training datasets, or optimization algorithms. Successfully transferring adversarial examples between models – such as generating a perturbation on one network and observing misclassification in a different network – demonstrates a broader, underlying weakness in the learning process itself, suggesting that models may be relying on features that are not robust or semantically meaningful.

The observed transferability of adversarial examples indicates that the underlying vulnerability extends beyond specific model implementations. This suggests the issue isn’t solely attributable to the architecture of neural networks, such as the number of layers or types of activation functions, nor is it limited to particular training methodologies. Instead, the susceptibility to adversarial perturbations appears to be a fundamental characteristic of the learning process itself, potentially stemming from the high dimensionality of input spaces, the non-robustness of decision boundaries, or the optimization algorithms used during training. This generality implies that defenses effective against attacks generated for one model may not generalize to others, necessitating a broader understanding of the root causes of this vulnerability.

Adversarial examples, generated using one model, frequently succeed in deceiving other, independently trained models – a phenomenon known as transferability. To efficiently evaluate this, VGG16 is employed as a surrogate model for generating adversarial inputs. When these inputs are applied to AlexNet, a substantial decrease in accuracy of 14.4% is observed. This performance degradation is comparable to the accuracy reductions seen when targeting classifiers based on Histogram of Oriented Gradients (HOG) features, indicating that the vulnerability to adversarial examples is not limited to specific neural network architectures or training methodologies.

Feature-engineered pipelines using HOG descriptors (RBF-SVM, K-NN, Linear SVM, and ANN) demonstrate comparable classification accuracy to CNN transfer learning baselines (AlexNet, VGG) under both clean and adversarial attacks (FGSM, PGD) when employing a block size of 3.
Feature-engineered pipelines using HOG descriptors (RBF-SVM, K-NN, Linear SVM, and ANN) demonstrate comparable classification accuracy to CNN transfer learning baselines (AlexNet, VGG) under both clean and adversarial attacks (FGSM, PGD) when employing a block size of 3.

Seeking Resilience: A Limited Intervention

Adversarial examples, initially crafted to deceive a deep learning model (VGG16), were then used as input to a distinct machine learning algorithm – a Decision Tree classifier. This transferability test was conducted using the CIFAR-10 dataset, a standard benchmark consisting of 60,000 32×32 color images categorized into 10 classes. The objective was to assess whether vulnerabilities exploited in deep neural networks could also impact algorithms relying on different feature extraction and classification methods. By evaluating performance degradation on the Decision Tree when presented with these transferred adversarial examples, we aimed to understand the broader implications of adversarial vulnerability beyond specific model architectures.

The CIFAR-10 dataset consists of 60,000 32×32 color images in 10 classes, with 6,000 images per class. Its relatively small size and established benchmark status facilitate efficient training and evaluation of machine learning models, enabling reproducible comparisons across different architectures and defense mechanisms. The dataset’s widespread use in computer vision research provides a common ground for assessing the robustness of models against adversarial attacks, allowing for objective measurement of performance degradation under perturbation. This standardization is critical for validating the transferability of adversarial examples and ensuring the reliability of robustness evaluations.

Evaluation of Histogram of Oriented Gradients (HOG)-based classifiers demonstrated vulnerability to Fast Gradient Sign Method (FGSM) adversarial attacks, with observed accuracy reductions ranging from 16.6% to 59.1% depending on configuration parameters. Critically, FGSM consistently induced larger decreases in accuracy than Projected Gradient Descent (PGD) attacks across all tested scenarios. This finding contradicts the expectation that feature-engineered pipelines, such as those utilizing HOG descriptors, inherently possess greater robustness against adversarial perturbations compared to end-to-end learned models.

Implementation of block normalization techniques resulted in a substantial improvement in Kernel Support Vector Machine (KSVM) accuracy when evaluated against adversarial attacks on the CIFAR-10 dataset. Specifically, accuracy gains ranged from 42% to 69% depending on the adversarial attack parameters and model configuration. This indicates that block normalization effectively mitigates the impact of adversarial perturbations on feature representations used by the KSVM classifier, offering a practical method for improving the robustness of feature-engineered machine learning pipelines.

Despite employing an aggressive perturbation budget of <span class="katex-eq" data-katex-display="false">\epsilon = 8/255</span>, adversarial attacks using FGSM and PGD maintain high cosine similarity (0.831 and 0.832 respectively) with original images, indicating preservation of global image structure.
Despite employing an aggressive perturbation budget of \epsilon = 8/255, adversarial attacks using FGSM and PGD maintain high cosine similarity (0.831 and 0.832 respectively) with original images, indicating preservation of global image structure.

The study reveals a troubling truth about system resilience. It suggests that attempts to fortify defenses – in this case, through meticulous feature engineering with HOG descriptors – are ultimately prophecies of eventual failure. The transferability of adversarial examples across fundamentally different computational paradigms-from neural networks to classical machine learning-highlights an inherent fragility. As Donald Davies observed, “Everything connected will someday fall together.” This isn’t merely about a vulnerability in HOG features; it’s a demonstration of how interconnectedness, even across ostensibly disparate systems, introduces cascading points of failure. The pursuit of robustness isn’t about building impenetrable walls, but understanding the ecosystem of dependencies and anticipating the inevitable propagation of vulnerabilities.

What’s Next?

The demonstrated transferability of adversarial examples-from the realm of learned feature spaces to those painstakingly hand-crafted-suggests a fundamental miscalculation in how robustness is approached. The pursuit of ‘defenses’ predicated on feature engineering now appears less a solution, and more a temporary relocation of the vulnerability. Stability is merely an illusion that caches well; the underlying fragility remains, exposed by the inevitable evolution of attack strategies.

Future work must abandon the notion of preventing adversarial perturbations. Such attempts are akin to building higher walls against a rising tide. Instead, research should focus on understanding the syntax of this chaos. If adversarial examples represent a natural language of vulnerability, then systems must learn to ‘read’-and perhaps even ‘speak’-it. A guarantee is just a contract with probability; the goal isn’t zero failures, but graceful degradation.

The observed cross-paradigm vulnerability implies a deeper, more general principle at play. This isn’t about specific algorithms or feature sets; it’s about the inherent limitations of any system attempting to map a continuous reality onto a discrete representation. Chaos isn’t failure-it’s nature’s syntax. The next stage requires a shift from brittle defenses to adaptable, self-aware systems capable of navigating-and even exploiting-the inherent uncertainty of the world.

Original article: https://arxiv.org/pdf/2601.21323.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-30 22:38