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 [1].
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
Authors: Ben Pinhasov, Raz Lapid, Rony Ohayon, Moshe Sipper, Yehudit Aperstein
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments to assess the performance of the deepfake detectors based on Xception Net and Efficient Net B4ST, following the methods described by Chollet (2017) and Bonettini et al. (2021). We used the Face Forensics++ (FF++) dataset (Rossler et al., 2019) to assess the effectiveness of our proposed face-forgery detection method. |
| Researcher Affiliation | Collaboration | Ben Pinhasov EMAIL Afeka, The Academic College of Engineering in Tel Aviv, Raz Lapid EMAIL Ben-Gurion University of the Negev, Beer-Sheva, 8410501, Israel & Deep Keep, Tel-Aviv, Israel, Rony Ohayon EMAIL Deep Keep, Tel-Aviv, Israel |
| Pseudocode | No | The paper only describes the methodology and algorithms in prose and through a flowchart (Figure 3), without specific pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/razla/XAI-Based-Detection-of-Adversarial-Attacks-on-Deepfake-Detectors |
| Open Datasets | Yes | We used the Face Forensics++ (FF++) dataset (Rossler et al., 2019) to assess the effectiveness of our proposed face-forgery detection method. |
| Dataset Splits | Yes | For our experiments, we randomly picked 60 videos from this dataset, with 50 allocated for training (including validation) and 10 for testing, resulting in 20,000 images for training and 5,000 images for testing, per each model and XAI configuration. |
| Hardware Specification | Yes | The experimental setup included a high-performance computing environment with an Intel Xeon Gold 6136 @ 3Ghz CPU, NVIDIA GRID P40-24Q, and 64GB of RAM. |
| Software Dependencies | Yes | The software environment consisted of Windows 10, Python 3.9, Pytorch 2.0.1, and the Captum library for implementing XAI techniques (Kokhlikyan et al., 2019). |
| Experiment Setup | Yes | Detect-Res Net50 model was trained with a learning rate of 0.001, a minibatch size of 16, and 100 iterations. We used the cross-entropy loss function, and the Adam optimizer (Kingma & Ba, 2014) with hyperparameters ϵ = 1e 8, β1 = 0.9, and β2 = 0.999. |