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AI Security
Understanding and Addressing Biases in Machine Learning
While ML offers extensive benefits, it also presents significant challenges, among them, one of the most prominent ones is biases…
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AI Security
Adversarial Attacks: The Hidden Risk in AI Security
Adversarial attacks specifically target the vulnerabilities in AI and ML systems. At a high level, these attacks involve inputting carefully…
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AI Security
Gradient-Based Attacks: A Dive into Optimization Exploits
Gradient-based attacks refer to a suite of methods employed by adversaries to exploit the vulnerabilities inherent in ML models, focusing…
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AI Security
The Unseen Dangers of GAN Poisoning in AI
GAN Poisoning is a unique form of adversarial attack aimed at manipulating Generative Adversarial Networks (GANs) during their training phase;…
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AI Security
“Magical” Emergent Behaviours in AI: A Security Perspective
Emergent behaviours in AI have left both researchers and practitioners scratching their heads. These are the unexpected quirks and functionalities…
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AI Security
How Dynamic Data Masking Reinforces Machine Learning Security
Data masking, also known as data obfuscation or data anonymization, serves as a crucial technique for ensuring data confidentiality and…
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AI Security
How Label-Flipping Attacks Mislead AI Systems
Label-flipping attacks refer to a class of adversarial attacks that specifically target the labeled data used to train supervised machine…
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AI Security
Backdoor Attacks in Machine Learning Models
Backdoor attacks in the context of Machine Learning (ML) refer to the deliberate manipulation of a model's training data or…
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AI Security
Perturbation Attacks in Text Classification Models
Text Classification Models are critical in a number of cybersecurity controls, particularly in mitigating risks associated with phishing emails and…
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AI Security
How Multimodal Attacks Exploit Models Trained on Multiple Data Types
In simplest terms, a multimodal model is a type of machine learning algorithm designed to process more than one type…
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AI Security
The Threat of Query Attacks on Machine Learning Models
Query attacks are a type of cybersecurity attack specifically targeting machine learning models. In essence, attackers issue a series of…
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AI Security
Securing Data Labeling Through Differential Privacy
Differential Privacy is a privacy paradigm that aims to reconcile the conflicting needs of data utility and individual privacy. Rooted…
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AI Security
Explainable AI Frameworks
Trust comes through understanding. As AI models grow in complexity, they often resemble a "black box," where their decision-making processes…
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AI Security
Meta-Attacks: Utilizing Machine Learning to Compromise Machine Learning Systems
Meta-attacks represent a sophisticated form of cybersecurity threat, utilizing machine learning algorithms to target and compromise other machine learning systems.…
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AI Security
How Saliency Attacks Quietly Trick Your AI Models
"Saliency" refers to the extent to which specific features or dimensions in the input data contribute to the final decision…
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