Search Results for author:
Found 10 papers, 3 papers with code
Adaptive White-Box Watermarking with Self-Mutual Check Parameters in Deep Neural Networks
Fragile watermarking is a technique used to identify tampering in AI models.
AdvFAS: A robust face anti-spoofing framework against adversarial examples
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques.
Augmenting Greybox Fuzzing with Generative AI
Large language models (LLM) pre-trained with an enormous amount of natural language corpus have proved to be effective for understanding the implicit format syntax and generating format-conforming inputs.
Decision-based iterative fragile watermarking for model integrity verification
Our method has some advantages: (1) the iterative update of samples is done in a decision-based black-box manner, relying solely on the predicted probability distribution of the target model, which reduces the risk of exposure to adversarial attacks, (2) the small-amplitude multiple iterations approach allows the fragile samples to perform well visually, with a PSNR of 55 dB in TinyImageNet compared to the original samples, (3) even with changes in the overall parameters of the model of magnitude 1e-4, the fragile samples can detect such changes, and (4) the method is independent of the specific model structure and dataset.
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
Neural network fragile watermarking with no model performance degradation
In the process of watermarking, we train a generative model with the specific loss function and secret key to generate triggers that are sensitive to the fine-tuning of the target classifier.
PalmTree: Learning an Assembly Language Model for Instruction Embedding
Deep learning has demonstrated its strengths in numerous binary analysis tasks, including function boundary detection, binary code search, function prototype inference, value set analysis, etc.
Boosting Adversarial Attacks on Neural Networks with Better Optimizer
However, the success rate of adversarial attacks can be further improved in black-box environments.
MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers
However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs).
Cryptography and Security
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not.
