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Found 9 papers, 1 papers with code
Detecting Deceptive Reviews using Generative Adversarial Networks
These results indicate that GANs can be effective for text classification tasks.
DifFuzz: Differential Fuzzing for Side-Channel Analysis
For this paper, we present an implementation that targets analysis of Java programs, and uses and extends the Kelinci and AFL fuzzers.
Cryptography and Security Software Engineering
Audio Attacks and Defenses against AED Systems -- A Practical Study
We show that an adversary can focus on audio adversarial inputs to cause AED systems to misclassify, achieving high success rates, even when we use small levels of a given type of noisy disturbance.
Attacks against Ranking Algorithms with Text Embeddings: a Case Study on Recruitment Algorithms
We also found that attacks against TF IDF is more successful compared to USE.
Evaluating the effectiveness of Phishing Reports on Twitter
Moreover, nearly 31% of these URLs were still active even after a week of them being reported, with 27% of them being detected by very few anti-phishing tools, suggesting that a large majority of these reports remain undiscovered, despite the majority of the follower base of these accounts being security focused users.
StyleGAN as a Utility-Preserving Face De-identification Method
In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers' perspectives.
Generating Phishing Attacks using ChatGPT
The ability of ChatGPT to generate human-like responses and understand context has made it a popular tool for conversational agents, content creation, data analysis, and research and innovation.
From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude
As a countermeasure, we build a BERT-based automated detection tool that can be used for the early detection of malicious prompts to prevent LLMs from generating phishing content.
Vulnerabilities Unveiled: Adversarially Attacking a Multimodal Vision Language Model for Pathology Imaging
In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted adversarial conditions.
