Targeted Adversarial Attack against Multimedia Recommender Systems (TAaMR)

GitHub repository of the DMSL2020 paper: Targeted Adversarial Attack against Multimedia Recommender Systems, published by Tommaso Di Noia, Daniele Malitesta and Felice Antonio Merra.

Paper available at Sisinflab publications web page.

The architectural overview of the proposed approach is as below:

Data preparation

To run the experimental section it is necessary to:

• Create in ./data/ a directory to store the dataset.
• Move in ./data/<dataset_name>/.
• Insert train and test file user_id\titem_id (users' and items indices need to be from 0 to N-1).
• Create directory ./data/<dataset_name>/original_images/images/.
• Store in ./data/<dataset_name>/original_images/images/ all the images using the same item_id in the format item_id.jpg.

Experiments

Operations to be executed (in src/):

• classify_extract.py - Executes the classification and the feature extraction on original images (the files classes.csv and features.npy are stored in ./data/<dataset_name>/original_images/).
• rec_generator.py - Generates the recommendation for the clean classes.
• results_analyzer.py - Evaluates the recommendation lists useful for select the origin and target class for the targeted adversarial attack.

Results evaluation

For the generation of each attack, run the script classify_extract_attack.py. It will create a new directory named ./data/<dataset_name>/<attack_name_parameters>/.

After each attack, run again rec_generator.py and results_analyzer.py to generate the new recommendations and evaluate these results. Additionally, run evaluate_visual_images.py to evaluate the visual metrics on the attacked images.

Implemented attacks

• FGSM
• PGD

Requirements

• Python 3.6.8
• CUDA 10.1
• cuDNN 7.6.4

All the requirements are in the file requirements.txt

pip install -r requirements.txt

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If you use this code, please cite us:

@inproceedings{DBLP:conf/dsn/NoiaMM20,
  author    = {Tommaso Di Noia and
               Daniele Malitesta and
               Felice Antonio Merra},
  title     = {TAaMR: Targeted Adversarial Attack against Multimedia Recommender
               Systems},
  booktitle = {50th Annual {IEEE/IFIP} International Conference on Dependable Systems
               and Networks Workshops, {DSN} Workshops 2020, Valencia, Spain, June
               29 - July 2, 2020},
  pages     = {1--8},
  publisher = {{IEEE}},
  year      = {2020},
  url       = {https://doi.org/10.1109/DSN-W50199.2020.00011},
  doi       = {10.1109/DSN-W50199.2020.00011},
  timestamp = {Mon, 03 Aug 2020 17:18:56 +0200},
  biburl    = {https://dblp.org/rec/conf/dsn/NoiaMM20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

An extension of this work has been presented at the 1st Workshop on Dataset Curation and Security co-located with the 34th Conference on Neural InformationProcessing Systems (NeurIPS 2020), Vancouver, Canada (Virtual Event). - dec 2020. Paper