Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks
Authors: Chetan Kumar, Riazat Ryan, Ming Shao11304-11311
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a popular visual social dataset have demonstrated that our defense strategy can significantly mitigate the impacts of family information leakage. |
| Researcher Affiliation | Academia | Chetan Kumar, Riazat Ryan, Ming Shao Department of Computer & Information Science University of Massachusetts Dartmouth, Dartmouth, MA, USA {ckumar, rryan2, mshao}@umassd.edu |
| Pseudocode | Yes | Algorithm 1: Procedure of Joint Adversarial Attack. |
| Open Source Code | No | The paper mentions using 'Sphere Net and GCN open implementation by (Kipf and Welling 2016; Liu et al. 2017)' but does not state that their own code for the described methodology is open-source or provide a link. |
| Open Datasets | Yes | In this study we have used Families In the Wild (FIW) dataset (Robinson et al. 2018). |
| Dataset Splits | Yes | Among 2758 nodes, we have used 502 nodes for training with graph, while the rest for validation and testing. |
| Hardware Specification | Yes | All the codes are implemented on Ubuntu16.04 system with i7-8700 (3.2 GHz), 16 GB memory and a nvidia GTX 1070 GPU card. |
| Software Dependencies | No | The paper mentions 'Py Torch library, Sphere Net and GCN open implementation' but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | First, we have preprocessed the FIW dataset by extracting the features of the images by using pre-trained Sphere Net model, and the dimension of node features is thus reduced to 512. ϵ = Δϵ = 0.00025, e = Δe = 0.05|E| from Algorithm 1. |