Modeling Deep Learning Based Privacy Attacks on Physical Mail
Authors: Bingyao Huang, Ruyi Lian, Dimitris Samaras, Haibin Ling1593-1601
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that hidden content details, such as texture and image structure, can be clearly recovered. Experimental Evaluations In this section, we quantitatively and qualitatively evaluate and compare the proposed Neural-STE with PSDNet (Guo et al. 2020), a learning-based image through scattering media method, Pix2pix (Isola et al. 2017), a general GANbased image-to-image translation model, Pix2pix HD (Wang et al. 2018), an improved version of Pix2pix, and degraded versions of the proposed method. |
| Researcher Affiliation | Academia | Bingyao Huang, Ruyi Lian, Dimitris Samaras, Haibin Ling Stony Brook University, NY, USA {bihuang, rulian, samaras, hling}@cs.stonybrook.edu |
| Pseudocode | No | The paper describes the model architecture and components in text and diagrams (Figure 2), but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code, benchmark dataset and experimental results are publicly available at https://github.com/Bingyao Huang/Neural-STE. |
| Open Datasets | Yes | The collected data is available as our Neural-STE dataset. We implement Neural-STE using Py Torch (Paszke et al. 2017) and Kornia (Riba et al. 2019), and optimize it using the Adam optimizer (Kingma and Ba 2015). The source code, benchmark dataset and experimental results are publicly available at https://github.com/Bingyao Huang/Neural-STE. |
| Dataset Splits | No | For each setup, we split the captured 500 image pairs into 450 training samples and 50 testing samples. (Only mentions training and testing samples, no explicit validation split.) |
| Hardware Specification | Yes | Then, we train the model for 4,000 iterations on three Nvidia Ge Force 1080Ti GPUs with a batch size of 16, taking about 18 minutes to train. |
| Software Dependencies | No | We implement Neural-STE using Py Torch (Paszke et al. 2017) and Kornia (Riba et al. 2019), and optimize it using the Adam optimizer (Kingma and Ba 2015). The paper mentions Py Torch and Kornia but does not specify exact version numbers for these libraries. |
| Experiment Setup | Yes | The proposed setup consists of a Canon 6D camera with the resolution set to 320 240. The initial learning rate and penalty factor are set to 10 3 and 5 10 4, respectively. Then, we train the model for 4,000 iterations on three Nvidia Ge Force 1080Ti GPUs with a batch size of 16, taking about 18 minutes to train. |