Detecting Adversarial Examples Through Image Transformation
Authors: Shixin Tian, Guolei Yang, Ying Cai
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments with two datasets show that our technique can detect nearly 99% of adversarial examples generated by the state-of-the-art algorithm. |
| Researcher Affiliation | Academia | Shixin Tian, Guolei Yang, Ying Cai Department of Computer Science, Iowa State University {stian,yanggl,yingcai}@iastate.edu |
| Pseudocode | No | The paper describes the method in text and a diagram (Figure 5) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of their methodology. |
| Open Datasets | Yes | The MNIST dataset has 70,000 handwritten digits from which 60,000 are used as the training set and 10,000 as the testing set. The CIFAR10 dataset consists of 60,000 colour images in 10 classes. 50,000 of them are used as the training set and the rest as the testing set. |
| Dataset Splits | No | The paper explicitly mentions training and testing sets for the main classifiers and the detector, but does not provide details on a specific validation set split. |
| Hardware Specification | Yes | When performing our experiments on a laptop with GPU (NVIDIA Ge Force GTX 960M), generating one adversarial example for oblivious threat model takes around 4 seconds while generating one adversarial example for white-box threat model takes around 75 seconds in average. |
| Software Dependencies | No | The paper states "The experiments are implemented with Keras using Tensor Flow as backend" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | These classifiers are trained at the learning rate of 0.01 with a batch size of 128 and 50 epochs. |