Image Feature Detectors for Deepfake Image Detection Using Transfer Learning

Alheeti, Khattab M. Ali; Al-Rawi, Salah Sleibi; Khalaf, Haitham Abbas; Dosary, Duaa Al

doi:10.1109/dese54285.2021.9719332

Cited by 2 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNN, LSTM, CNN-LSTM, and CNN-RNN also showed reasonable results, but their performance The results for BoderlineSMOTE explain that the CNN-DBN of [35] and KNN produced massive results as displayed in Table 4. For DL, the CNN-DBN model has the best accuracy, precision, recall, F-1, and G-mean scores of 0.873, 0.882, 0.874, 0.875, and 0.870 respectively when compared with other DL models and when we compare the results of KNN with [34], [23] and other traditional ML algorithms, there is a huge difference as KNN has better evaluation results.…”

Section: Resultsmentioning

confidence: 96%

“…The second experiment was performed on other models using four strategies: SMOTE, BorderlineSMOTE, SMOTEENN, and SMOTE-Tomek. We compared the results of [34], [23], [35], and some other models on which we have performed the experiments to compare their performance. The models were then evaluated and the results were compared by using the Multi-step cyber attack dataset.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

Jamal,

Naz,

Khattak

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The increasing dependence on data analytics and artificial intelligence (AI) methodologies across various domains has prompted the emergence of apprehensions over data security and integrity. There exists a consensus among scholars and experts that the identification and mitigation of Multi-step attacks pose significant challenges due to the intricate nature of the diverse approaches utilized. This study aims to address the issue of imbalanced datasets within the domain of Multi-step attack detection. To achieve this objective, the research explores three distinct re-sampling strategies, namely over-sampling, undersampling, and hybrid re-sampling techniques. The study offers a comprehensive assessment of several re-sampling techniques utilized in the detection of Multi-step attacks on deep learning (DL) models. The efficacy of the solution is evaluated using a Multi-step cyber attack dataset that emulates attacks across six attack classes. Furthermore, the performance of several re-sampling approaches with numerous traditional machine learning (ML) and deep learning (DL) models are compared, based on performance metrics such as accuracy, precision, recall, F-1 score, and G-mean. In contrast to preliminary studies, the research focuses on Multi-step attack detection. The results indicate that the combination of Convolutional Neural Networks (CNN) with Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN) provides optimal results as compared to standalone ML/DL models. Moreover, the results also depict that SMOTEENN, a hybrid re-sampling technique, demonstrates superior effectiveness in enhancing detection performance across various models and evaluation metrics. The findings indicate the significance of appropriate re-sampling techniques to improve the efficacy of Multistep attack detection on DL models.

show abstract

Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%