Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …
there is a great need to deliberate on the future of the cities worth living. In particular, as …
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
The false promise of imitating proprietary llms
An emerging method to cheaply improve a weaker language model is to finetune it on
outputs from a stronger model, such as a proprietary system like ChatGPT (eg, Alpaca, Self …
outputs from a stronger model, such as a proprietary system like ChatGPT (eg, Alpaca, Self …
[HTML][HTML] Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …
resulted in algorithms being adopted for resolving a variety of problems. However, this …
Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Protecting language generation models via invisible watermarking
Abstract Language generation models have been an increasingly powerful enabler to many
applications. Many such models offer free or affordable API access which makes them …
applications. Many such models offer free or affordable API access which makes them …
A survey of privacy attacks in machine learning
As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …
security and privacy becomes more urgent. Although the body of work in privacy has been …
Bppattack: Stealthy and efficient trojan attacks against deep neural networks via image quantization and contrastive adversarial learning
Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns
(eg, a patch or image transformations) as triggers, which are vulnerable to human …
(eg, a patch or image transformations) as triggers, which are vulnerable to human …
APMSA: Adversarial perturbation against model stealing attacks
Training a Deep Learning (DL) model requires proprietary data and computing-intensive
resources. To recoup their training costs, a model provider can monetize DL models through …
resources. To recoup their training costs, a model provider can monetize DL models through …
High accuracy and high fidelity extraction of neural networks
In a model extraction attack, an adversary steals a copy of a remotely deployed machine
learning model, given oracle prediction access. We taxonomize model extraction attacks …
learning model, given oracle prediction access. We taxonomize model extraction attacks …