Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Xu, Yong; Zhang, Zheng; Wang, Xunguang; Wu, Lin

doi:10.1109/tifs.2023.3297791

Cited by 8 publications

(2 citation statements)

References 41 publications

(92 reference statements)

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“…Yuan et al [17] delve into the realm of semantic-aware adversarial training, focusing on deep hashing retrieval. Their approach to enhancing the reliability of deep hashing retrieval systems through adversarial training marks a significant step in the field of information security.…”

Section: Review Of Existing Models For Adversarial Attack Analysismentioning

confidence: 99%

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

Chetan Patil

2023

IJRITCC

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In the realm of cybersecurity, the escalating sophistication of adversarial attacks poses a significant threat, particularly in the context of machine learning models. Traditional defensive mechanisms often fall short in identifying and mitigating such attacks, primarily due to their static nature and inability to adapt to the evolving strategies of adversaries. This limitation underscores the necessity for more dynamic and responsive approaches. Addressing this critical gap, our research introduces an innovative Active Machine Learning Adversarial Attack Detection framework process. Central to our approach is the strategic amalgamation of data collection and preprocessing techniques. We meticulously gather a diverse dataset encompassing both genuine and adversarial user feedback, which is then carefully annotated to differentiate between the two scenarios. This data undergoes rigorous preprocessing, including tokenization and conversion into numerical features through methods like TF-IDF and word embeddings, paving the way for more nuanced analysis. The core of our model employs a variety of machine learning algorithms—Logistic Regression, Random Forest, SVM, CNN, and XGBoost—each fine-tuned through meticulous hyperparameter optimizations. The novelty of our approach, however, lies in the integration of an active learning strategy for efficient results. By employing uncertainty sampling and query-by-committee, our model actively identifies and learns from instances of highest informational value, continuously evolving in its detection capabilities. Our framework further stands out in its post-training phases. The models are not only retrained with newly labeled data but are also subjected to a comprehensive evaluation on separate test datasets. Metrics such as accuracy, precision, recall, F1-score, and AUC are meticulously computed, ensuring the robustness of our results. Deployed in a real-time environment, the model demonstrates remarkable efficacy in detecting adversarial attacks in user feedback. Continuous monitoring and periodic retraining allow the model to adapt and respond to new adversarial tactics. The impact of our work is quantitatively significant—our model outperforms existing methods with a 9.5% improvement in precision, 8.5% higher accuracy, 8.3% increased recall, 9.4% greater AUC, 4.5% higher specificity, and a 2.9% reduction in detection delays for different scenarios.

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Section: Review Of Existing Models For Adversarial Attack Analysismentioning

confidence: 99%

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

Chetan Patil

2023

IJRITCC

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“…Therefore, it is crucial to research adversarial defense methods for DLSS to enhance the model’s ability to defend against adversarial attacks and establish robust soft-sensor models that are resilient to such attacks. Adversarial training is the primary method for enhancing the adversarial robustness of deep learning models, recognized as one of the most effective adversarial defense methods [ 21 ]. The samples generated from normal samples subjected to adversarial attacks are called adversarial samples.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation

Guo,

Chen,

Liu

et al. 2024

Sensors

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Despite their high prediction accuracy, deep learning-based soft sensor (DLSS) models face challenges related to adversarial robustness against malicious adversarial attacks, which hinder their widespread deployment and safe application. Although adversarial training is the primary method for enhancing adversarial robustness, existing adversarial-training-based defense methods often struggle with accurately estimating transfer gradients and avoiding adversarial robust overfitting. To address these issues, we propose a novel adversarial training approach, namely domain-adaptive adversarial training (DAAT). DAAT comprises two stages: historical gradient-based adversarial attack (HGAA) and domain-adaptive training. In the first stage, HGAA incorporates historical gradient information into the iterative process of generating adversarial samples. It considers gradient similarity between iterative steps to stabilize the updating direction, resulting in improved transfer gradient estimation and stronger adversarial samples. In the second stage, a soft sensor domain-adaptive training model is developed to learn common features from adversarial and original samples through domain-adaptive training, thereby avoiding excessive leaning toward either side and enhancing the adversarial robustness of DLSS without robust overfitting. To demonstrate the effectiveness of DAAT, a DLSS model for crystal quality variables in silicon single-crystal growth manufacturing processes is used as a case study. Through DAAT, the DLSS achieves a balance between defense against adversarial samples and prediction accuracy on normal samples to some extent, offering an effective approach for enhancing the adversarial robustness of DLSS.

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Introduction

Zhang

2024

Binary Representation Learning on Visual Images

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Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Cited by 8 publications

References 41 publications

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments

Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation

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