Towards Understanding Deep Learning Model in Image Recognition via Coverage Test
cs.CV, cs.AI, cs.LG
Submitted: 2025-05-12
Updated: 2026-10-12
Sources
- Longformer: The Long-Document Transformer
- Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM
- SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode
- Rethinking Attention with Performers
- Explaining and Harnessing Adversarial Examples
- SCALM: Detecting Bad Practices in Smart Contracts Through LLMs
- SoK: Security Analysis of Blockchain-based Cryptocurrency
- Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications
- SCLA: Automated Smart Contract Summarization via LLMs and Control Flow Prompt
- Efficient Estimation of Word Representations in Vector Space
- Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth
- Smart Contract Vulnerability Detection Technique: A Survey
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
- Testing Deep Neural Networks
- Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges
- Smart Contracts in the Real World: A Statistical Exploration of External Data Dependencies
- Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection
- ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts
- Malicious Code Detection in Smart Contracts via Opcode Vectorization
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