Guiding the retraining of convolutional neural networks against
adversarial inputs
- URL: http://arxiv.org/abs/2207.03689v2
- Date: Tue, 12 Jul 2022 04:39:21 GMT
- Title: Guiding the retraining of convolutional neural networks against
adversarial inputs
- Authors: Francisco Dur\'an L\'opez, Silverio Mart\'inez-Fern\'andez, Michael
Felderer and Xavier Franch
- Abstract summary: We examined four guidance metrics for retraining convolutional neural networks and three retraining configurations.
Our goal is to improve the models against adversarial inputs regarding accuracy, resource utilization and time.
- Score: 9.67555836316884
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: When using deep learning models, there are many possible
vulnerabilities and some of the most worrying are the adversarial inputs, which
can cause wrong decisions with minor perturbations. Therefore, it becomes
necessary to retrain these models against adversarial inputs, as part of the
software testing process addressing the vulnerability to these inputs.
Furthermore, for an energy efficient testing and retraining, data scientists
need support on which are the best guidance metrics and optimal dataset
configurations.
Aims: We examined four guidance metrics for retraining convolutional neural
networks and three retraining configurations. Our goal is to improve the models
against adversarial inputs regarding accuracy, resource utilization and time
from the point of view of a data scientist in the context of image
classification.
Method: We conducted an empirical study in two datasets for image
classification. We explore: (a) the accuracy, resource utilization and time of
retraining convolutional neural networks by ordering new training set by four
different guidance metrics (neuron coverage, likelihood-based surprise
adequacy, distance-based surprise adequacy and random), (b) the accuracy and
resource utilization of retraining convolutional neural networks with three
different configurations (from scratch and augmented dataset, using weights and
augmented dataset, and using weights and only adversarial inputs).
Results: We reveal that retraining with adversarial inputs from original
weights and by ordering with surprise adequacy metrics gives the best model
w.r.t. the used metrics.
Conclusions: Although more studies are necessary, we recommend data
scientists to use the above configuration and metrics to deal with the
vulnerability to adversarial inputs of deep learning models, as they can
improve their models against adversarial inputs without using many inputs.
Related papers
- Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data [38.44734564565478]
We provide a theoretical understanding of adversarial examples and adversarial training algorithms from the perspective of feature learning theory.
We show that the adversarial training method can provably strengthen the robust feature learning and suppress the non-robust feature learning.
arXiv Detail & Related papers (2024-10-11T03:59:49Z) - A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks [81.2624272756733]
In dense retrieval, deep encoders provide embeddings for both inputs and targets.
We train a small parametric corrector network that adjusts stale cached target embeddings.
Our approach matches state-of-the-art results even when no target embedding updates are made during training.
arXiv Detail & Related papers (2024-09-03T13:29:13Z) - Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural
Networks [12.525959293825318]
We introduce Learn, Unlearn, and Relearn (LURE) an online learning paradigm for deep neural networks (DNNs)
LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model, and the relearning phase, which emphasizes learning on generalizable features.
We show that our training paradigm provides consistent performance gains across datasets in both classification and few-shot settings.
arXiv Detail & Related papers (2023-03-18T16:45:54Z) - DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning [5.2319020651074215]
We propose a Curricumum-guided Contrastive Learning framework for neural Predictor (DCLP)
Our method simplifies the contrastive task by designing a novel curriculum to enhance the stability of unlabeled training data distribution.
We experimentally demonstrate that DCLP has high accuracy and efficiency compared with existing predictors.
arXiv Detail & Related papers (2023-02-25T08:16:21Z) - Towards Robust Dataset Learning [90.2590325441068]
We propose a principled, tri-level optimization to formulate the robust dataset learning problem.
Under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset.
arXiv Detail & Related papers (2022-11-19T17:06:10Z) - Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
We construct a dataset of neural network checkpoints and train a generative model on the parameters.
We find that our approach successfully generates parameters for a wide range of loss prompts.
We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
arXiv Detail & Related papers (2022-09-26T17:59:58Z) - Distributed Adversarial Training to Robustify Deep Neural Networks at
Scale [100.19539096465101]
Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification.
To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training.
We propose a large-batch adversarial training framework implemented over multiple machines.
arXiv Detail & Related papers (2022-06-13T15:39:43Z) - MLDS: A Dataset for Weight-Space Analysis of Neural Networks [0.0]
We present MLDS, a new dataset consisting of thousands of trained neural networks with carefully controlled parameters.
This dataset enables new insights into both model-to-model and model-to-training-data relationships.
arXiv Detail & Related papers (2021-04-21T14:24:26Z) - Attribute-Guided Adversarial Training for Robustness to Natural
Perturbations [64.35805267250682]
We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space.
Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
arXiv Detail & Related papers (2020-12-03T10:17:30Z) - Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions.
We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples.
We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
arXiv Detail & Related papers (2020-06-13T08:24:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.