Ask people on the street how much AI uses today affect their lives, and most would probably answer that it doesn’t affect them right now. Some might say that it’s pure science fiction. Others might say that it may affect our future but isn’t used in our world today. Some might correctly identify a few ways it’s used in modern technology, such as voice-powered personal assistants like Siri, Alexa and Cortana. But most would be surprised to find out how widely it is already woven into the fabric of daily life.
Whether AI and the technologies it enables will reach their full potential depends on the workforce that will work alongside them. Yet the skills...
Data masking, also known as data obfuscation or data anonymization, serves as a crucial technique for ensuring data confidentiality and integrity, particularly in non-production environments like development, testing, and analytics. It operates by replacing actual sensitive data with a sanitized version, rendering the data ineffective for malicious exploitation while retaining its functional utility for testing or analysis.
Backdoor attacks in the context of Machine Learning (ML) refer to the deliberate manipulation of a model's training data or its algorithmic logic to implant a hidden vulnerability, often referred to as a "trigger." Unlike typical vulnerabilities that are discovered post-deployment, backdoor attacks are often premeditated and planted during the model's development phase. Once deployed, the compromised ML model appears to function normally for standard inputs. However, when the model encounters a specific input pattern corresponding to the embedded trigger, it produces an output that is intentionally skewed or altered, thereby fulfilling the attacker's agenda.
If you've ever been to an expensive restaurant and ordered a familiar dish like, say, lasagna, but received a plate with five different elements...
Data spoofing is the intentional manipulation, fabrication, or misrepresentation of data with the aim of deceiving systems into making incorrect decisions or assessments. While it is often associated with IP address spoofing in network security, the concept extends into various domains and types of data, including, but not limited to, geolocation data, sensor readings, and even labels in machine learning datasets. In the realm of cybersecurity, the most commonly spoofed types of data include network packets, file hashes, digital signatures, and user credentials. The techniques used for data spoofing are varied and often sophisticated,
Batch exploration attacks are a class of cyber attacks where adversaries systematically query or probe streamed machine learning models to expose vulnerabilities, glean sensitive information, or decipher the underlying structure and parameters of the models. The motivation behind such attacks often stems from a desire to exploit vulnerabilities in streamed data models for unauthorized access, information extraction, or model manipulation, given the wealth of real-time and dynamic data these models process. The ramifications of successful attacks can be severe, ranging from loss of sensitive and proprietary information and erosion of user trust to substantial financial repercussions.
In the summer of 1956, a small gathering of researchers and scientists at Dartmouth College, a small yet prestigious Ivy League school in Hanover, New Hampshire, ignited a spark that would forever change the course of human history. This historic event, known as the Dartmouth Workshop, is widely regarded as the birthplace of artificial intelligence (AI) and marked the inception of a new field of study that has since started revolutionizing countless aspects of our lives.
GAN Poisoning is a unique form of adversarial attack aimed at manipulating Generative Adversarial Networks (GANs) during their training phase; unlike traditional cybersecurity threats like data poisoning or adversarial input attacks, which either corrupt training data or trick already-trained models, GAN Poisoning focuses on altering the GAN's generative capability to produce deceptive or harmful outputs. The objective is not merely unauthorized access but the generation of misleading or damaging information.
Data poisoning is a targeted form of attack wherein an adversary deliberately manipulates the training data to compromise the efficacy of machine learning models. The training phase of a machine learning model is particularly vulnerable to this type of attack because most algorithms are designed to fit their parameters as closely as possible to the training data. An attacker with sufficient knowledge of the dataset and model architecture can introduce 'poisoned' data points into the training set, affecting the model's parameter tuning. This leads to alterations in the model's future performance that align with the attacker’s objectives, which could range from making incorrect predictions and misclassifications to more sophisticated outcomes like data leakage or revealing sensitive information.
Adversarial attacks specifically target the vulnerabilities in AI and ML systems. At a high level, these attacks involve inputting carefully crafted data into an AI system to trick it into making an incorrect decision or classification. For instance, an adversarial attack could manipulate the pixels in a digital image so subtly that a human eye wouldn't notice the change, but a machine learning model would classify it incorrectly, say, identifying a stop sign as a 45-mph speed limit sign, with potentially disastrous consequences in an autonomous driving context.
Emergent behaviours in AI have left both researchers and practitioners scratching their heads. These are the unexpected quirks and functionalities that pop up in complex AI systems, not because they were explicitly trained to exhibit them, but due to the intricate interplay of the system's complexity, the sheer volume of data it sifts through, and its interactions with other systems or variables. It's like giving a child a toy and watching them use it to build a skyscrapper. While scientists hoped that scaling up AI models would enhance their performance on familiar tasks, they were taken aback when these models started acing a number of unfamiliar tasks.
Growing reliance on AI will not likely result in any of the three most common views of how AI will affect our future. Each...