Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
Authors: Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on three benchmark data sets: CIFAR-10 [18] with CIFAR-100 [18] as proxy, Fashion-MNIST [37] with CIFAR-10 [18] as proxy and 10 Monkey Species [27] with Celeb A-HQ [13] and Image Net Cats and Dogs [33] as proxies. |
| Researcher Affiliation | Academia | Antonio B arb al au1, , Adrian Cosma2, Radu Tudor Ionescu1, Marius Popescu1 1University of Bucharest, Romania 2University Politehnica of Bucharest, Romania |
| Pseudocode | Yes | Algorithm 1 Evolutionary Optimization Algorithm |
| Open Source Code | Yes | Our code is available at: https://github.com/antoniobarbalau/black-box-ripper. |
| Open Datasets | Yes | We conduct experiments on three benchmark data sets: CIFAR-10 [18] with CIFAR-100 [18] as proxy, Fashion-MNIST [37] with CIFAR-10 [18] as proxy and 10 Monkey Species [27] with Celeb A-HQ [13] and Image Net Cats and Dogs [33] as proxies. |
| Dataset Splits | No | The paper defines 'original (or true) training set', 'original (or true) test set', and 'proxy training set', but does not specify details regarding a validation dataset split or its proportion. |
| Hardware Specification | No | The paper describes the neural network architectures used (e.g., Alex Net, VGG-16, Le Net, Res Net-18) but does not provide specific details about the hardware, such as GPU or CPU models, used for running the experiments. |
| Software Dependencies | No | The paper mentions various models and optimizers like VAE [16], GAN [7], Adam [15], Progressively Growing GAN (Pro GAN) [13], Spectral Normalization GAN (SNGAN) [26], but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | All models, including baselines, are trained for 200 epochs using the Adam [15] optimizer. We used mini-batches of 64 images. In Black-Box Ripper, the images are synthesized by the evolutionary algorithm, using at most 10 iterations, a population of K = 30 latent vectors sampled within the boundary u = 3, an elite size of k = 10, halting the optimization if the ļ¬ttest latent vector gives an objective value lower than t = 0.02. |