Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks
Authors: Chetan Kumar, Riazat Ryan, Ming Shao11304-11311
AAAI 2020 | Venue PDF | 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 EMAIL |
| 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. |