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Found 7 papers, 4 papers with code
Towards an Accurate and Secure Detector against Adversarial Perturbations
It expands the above works on two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability and thereby is more suitable for capturing adversarial patterns than the common trigonometric or wavelet basis; 2) the extensive parameters for decomposition are generated by a pseudo-random function with secret keys, hence blocking the defense-aware adversarial attack.
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection
In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation.
SOM-based DDoS Defense Mechanism using SDN for the Internet of Things
To effectively tackle the security threats towards the Internet of things, we propose a SOM-based DDoS defense mechanism using software-defined networking (SDN) in this paper.
Boosting API Recommendation with Implicit Feedback
In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance.
DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
The source code suggestions provided by current IDEs are mostly dependent on static type learning.
Deep Transfer Learning for Source Code Modeling
A challenging issue of these approaches is that they require training from starch for a different related problem.
CodeGRU: Context-aware Deep Learning with Gated Recurrent Unit for Source Code Modeling
We evaluate CodeGRU with real-world data set and it shows that CodeGRU outperforms the state-of-the-art language models and help reduce the vocabulary size up to 24. 93\%.
