Robust DeepFake Face Detection Leveraging Xception Model and Novel Snake Optimization Technique

Ahmed SAAD Al-Qazzaz; Pedram Salehpour; Hadi S. Aghdasi

doi:10.18196/jrc.v5i5.22473

Authors

Ahmed SAAD Al-Qazzaz University of Tabriz
Pedram Salehpour University of Tabriz
Hadi S. Aghdasi University of Tabriz

DOI:

https://doi.org/10.18196/jrc.v5i5.22473

Keywords:

DeepFake Face Detection, Xception Model, Snake Optimization, Digital Manipulation, Deep Learning, Media Forensics, Video Authentication.

Abstract

DeepFake technology has created an existential crisis around authenticity in digital media with the ability to create nearly imperceptible forgeries on a massive scale, such as impersonating public figures for nefarious reasons like misinformation campaigns, harassment, and fraud. In this thesis, a model Xception is combined with the Snake optimization technique to ensure efficient and accurate detection of ADOR in practice. The former is deep CNN architecture Xception which exploits depthwise separable convolutions to perform efficient feature extraction, and the latter is a novel snake optimization that borrows lessons from real-life predatory snakes to dynamically adapt parameters for better exploration of search space while avoiding local optima. The combined modality is systematically evaluated using multiple challenging DeepFake video datasets and shows significant improvement. A comparison of performance with other methods showed that a mean accuracy, precision, recall, and F1-score was 98.53% for the Snake-optimized Xception model while outperformed some state-of-the-art approaches and traditional Xception itself. This helps in reducing missing of misdetection and reduction of false positives, helping achieve a tool that is highly effective for digital media forensics. Such discoveries open the door for this method to unlock new levels of digital content integrity, necessary in media verification and legal evidence authentication, as well as assist individuals dealing with fake news or videos attempting identity theft online. This research highlights the strong efficacy of coupling the Xception model with Snake optimization for DeepFake detection; thus, establishes a new state-of-the-art and will inspire future studies and applications to protect genuineness in digital media.

References

J. Banumathi et al., "An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification," CMC, vol. 67, no. 2, pp. 2393–2407, 2021, doi: 10.32604/cmc.2021.015605.

B. U. Mahmud and A. Sharmin, "Deep Insights of Deepfake Technology: A Review," arXiv preprint arXiv:2105.00192, 2021.

M. Dang and T. N. Nguyen, "Digital Face Manipulation Creation and Detection: A Systematic Review," Electronics, vol. 12, no. 16, p. 3407, 2023, doi: 10.3390/electronics12163407.

E. Ferrara, S. Cresci, and L. Luceri, "Misinformation, manipulation, and abuse on social media in the era of COVID-19," J. Comput. Soc. Sci., vol. 3, no. 2, pp. 271–277, 2020, doi: 10.1007/s42001-020-00094-5.

E. U. H. Qazi, T. Zia, and A. Almorjan, "Deep Learning-Based Digital Image Forgery Detection System," Appl. Sci., vol. 12, no. 6, p. 2851, 2022, doi: 10.3390/app12062851.

F. Juefei-Xu, R. Wang, Y. Huang, Q. Guo, L. Ma, and Y. Liu, "Countering malicious deepfakes: Survey, battleground, and horizon," International Journal of Computer Vision, vol. 130, no. 7, pp. 1678-1734, 2022.

L. Peng et al., "A Multi-strategy Improved Snake Optimizer Assisted with Population Crowding Analysis for Engineering Design Problems," J. Bionic Eng., vol. 21, no. 3, pp. 1567–1591, 2024, doi: 10.1007/s42235-024-00505-7.

D. Pan, L. Sun, R. Wang, X. Zhang, and R. O. Sinnott, "Deepfake detection through deep learning," in 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), pp. 134-143, 2020.

A. Diwan and U. Sonkar, "Visualizing the truth: a survey of multimedia forensic analysis," Multimed. Tools Appl., vol. 83, no. 16, pp. 47979–48006, 2024, doi: 10.1007/s11042-023-17475-3.

G. Hu, R. Yang, M. Abbas, and G. Wei, "BEESO: Multi-strategy Boosted Snake-Inspired Optimizer for Engineering Applications," J. Bionic Eng., vol. 20, no. 4, pp. 1791–1827, 2023, doi: 10.1007/s42235-022-00330-w.

K. Shaheed, A. Mao, I. Qureshi, M. Kumar, S. Hussain, I. Ullah, and X. Zhang, “DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition,” Expert Systems with Applications, vol. 191, p. 116288, 2022.

M. M. El-Gayar, M. Abouhawwash, S. S. Askar, and S. Sweidan, "A novel approach for detecting deep fake videos using graph neural network," J. Big Data, vol. 11, no. 1, pp. 1–27, 2024, doi: 10.1186/s40537-024-00884-y.

O. A. H. H. Al-Dulaimi and S. Kurnaz, "A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning," Electronics, vol. 13, no. 9, p. 1662, 2024, doi: 10.3390/electronics13091662.

M. Tian, M. Khayatkhoei, J. Mathai, and W. AbdAlmageed, "Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies," arXiv, vol. 2311.17088, 2023.

H. Jeon, Y. Bang, and S. S. Woo, "FDFtNet: Facing Off Fake Images Using Fake Detection Fine-Tuning Network," ICT Systems Security and Privacy Protection, 2020, doi: 10.1007/978-3-030-58201-2_28.

M. Masood, M. Nawaz, K. M. Malik, A. Javed, A. Irtaza, and H. Malik, "Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward," Appl. Intell., vol. 53, no. 4, pp. 3974–4026, 2023, doi: 10.1007/s10489-022-03766-z.

X. Wu, X. Liao, B. Ou, Y. Liu, and Z. Qin, "Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics," arXiv preprint arXiv:2404.17867, 2024.

M. Tayseer, J. Mohammad, M. Ababneh, A. Al-Zoube, and A. Elhassan, “Digital Forensics and Analysis of Deepfake Videos,” in 11th International Conference on Information and Communication Systems (ICICS), 2020.

L. Bojic, N. Prodanovic, and A. D. Samala, "Maintaining Journalistic Integrity in the Digital Age: A Comprehensive NLP Framework for Evaluating Online News Content," arXiv preprint arXiv:2401.03467, 2024.

Y. Wang, V. Zarghami, and S. Cui, "Fake Face Detection using Local Binary Pattern and Ensemble Modeling," in 2021 IEEE International Conference on Image Processing (ICIP), pp. 3917-3921, 2021, doi: 10.1109/ICIP42928.2021.9506460.

P. Theerthagiri and G. Nagaladinne, "Deepfake Face Detection Using Deep InceptionNet Learning Algorithm," in 2023 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1-6, 2023, doi: 10.1109/SCEECS57921.2023.10063128.

M. Taeb and H. Chi, "Comparison of Deepfake Detection Techniques through Deep Learning," J. Cybersecur. Priv., vol. 2, no. 1, pp. 89–106, 2022, doi: 10.3390/jcp2010007.

P. M. Arunkumar, Y. Sangeetha, P. V. Raja, and S. N. Sangeetha, "Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph," ITC, vol. 51, no. 3, pp. 563–574, 2022, doi: 10.5755/j01.itc.51.3.31510.

V. Nagagopiraju, K. Ayyappa, P. Anshulalitha, J. Srikanth, and K. T. Teja, "A Effciet Deep Fake Face Detection Using Deep Inception Net Learning Algorithm," Turcomat, vol. 15, no. 1, pp. 138–141, 2024, doi: 10.61841/turcomat.v15i1.14555.

S. Pei et al., "A bidirectional-LSTM method based on temporal features for deep fake face detection in videos," in Proceedings Volume 12346, 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), vol. 12346, pp. 311-318, 2022, doi: 10.1117/12.2653461.

A. Ismail, M. Elpeltagy, M. S. Zaki, and K. Eldahshan, "A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost," Sensors, vol. 21, no. 16, p. 5413, 2021, doi: 10.3390/s21165413.

L. Guarnera et al., "The Face Deepfake Detection Challenge," J. Imaging, vol. 8, no. 10, p. 263, 2022, doi: 10.3390/jimaging8100263.

S. T. Suganthi, M. U. A. Ayoobkhan, N. Bacanin, K. Venkatachalam, H. Štěpán, and T. Pavel, "Deep learning model for deep fake face recognition and detection," PeerJ Comput. Sci., vol. 8, p. e881, 2022, doi: 10.7717/peerj-cs.881.

Z. Guo, G. Yang, J. Chen, and X. Sun, "Fake face detection via adaptive manipulation traces extraction network," Comput. Vision Image Understanding, vol. 204, p. 103170, 2021, doi: 10.1016/j.cviu.2021.103170.

A. Khormali and J.-S. Yuan, "ADD: Attention-Based DeepFake Detection Approach," Big Data Cogn. Comput., vol. 5, no. 4, p. 49, 2021, doi: 10.3390/bdcc5040049.

A. Shah, S. Varshney, and M. Mehrotra, "Detection of Fake Profiles on Online Social Network Platforms: Performance Evaluation of Artificial Intelligence Techniques," SN Comput. Sci., vol. 5, no. 5, pp. 1–15, 2024, doi: 10.1007/s42979-024-02839-9.

H. Else, “Publishers unite to tackle doctored images in research papers,” Nature, 2021, doi: 10.1038/d41586-021-02610-7.

C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, p. 652801, 2021, doi: 10.3389/fenrg.2021.652801

SQL-Injection-Extend. (2024, May 17). Retrieved from https://www.kaggle.com/datasets/ciplab/real-and-fake-face-detection

V. W. de Vargas, J. A. S. Aranda, R. D. S. Costa, P. R. da Silva Pereira, and J. L. V. Barbosa, “Imbalanced data preprocessing techniques for machine learning: a systematic mapping study,” Knowl. Inf. Syst., vol. 65, no. 1, pp. 31–57, 2023, doi: 10.1007/s10115-022-01772-8.

S. Sharma and S. Kumar, “The Xception model: A potential feature extractor in breast cancer histology images classification,” ICT Express, vol. 8, no. 1, pp. 101-108, 2022.

A. Dhillon G. K. Verma, “Convolutional neural network: a review of models, methodologies and applications to object detection,” Progress in Artificial Intelligence, vol. 9, no. 2, pp. 85-112, 2020.

S. Pashine, S. Mandiya, P. Gupta, and R. Sheikh, “Deep fake detection: survey of facial manipulation detection solutions,” arXiv preprint arXiv:2106.12605, 2021.

A. Satapathy and J. Livingston LM, “A lightweight convolutional neural network built on inceptio-residual and reduction modules for deep facial recognition in realistic conditions,” The Imaging Science Journal, vol. 71, no. 1, pp. 14-32, 2023.

C. E. S. Rex, J. Annrose, and J. J. Jose, “Comparative analysis of deep convolution neural network models on small scale datasets,” Optik, vol. 271, p. 170238, 2022.

P. Sumari et al., "A Precision Agricultural Application: Manggis Fruit Classification Using Hybrid Deep Learning," Rev. d'Intelligence Artif., vol. 35, no. 5, pp. 375-381, 2021.

R. Adityatama and A. T. Putra, "Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning," Recursive Journal of Informatics, vol. 1, no. 2, pp. 102-109, 2023.

D. M. W. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv preprint arXiv:2010.16061, 2020.

F. A. Hashim and A. G. Hussien, "Snake Optimizer: A novel meta-heuristic optimization algorithm," Knowledge-Based Systems, vol. 242, p. 108320, 2022.

"CSDL | IEEE Computer Society," May 08, 2024.

B. Bischl et al., "Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 13, no. 2, p. e1484, 2023.

L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295-316, 2020.

G. P. Bhandari, R. Gupta, and S. K. Upadhyay, "An approach for fault prediction in SOA-based systems using machine learning techniques," Data Technologies and Applications, vol. 53, no. 4, pp. 397-421, 2019.

H. Shavit, F. Jatelnicki, P. Mor-Puigventós, and W. Kowalczyk, "From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search," arXiv preprint arXiv:2212.08448, 2022.

D. Krstinić, M. Braović, L. Šerić, and D. Božić-Štulić, "Multi-label classifier performance evaluation with confusion matrix," Computer Science & Information Technology, vol. 1, pp. 1-14, 2020.

J. Görtler et al., "Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels," arXiv preprint arXiv:2110.12536, 2021.

S. A. Macskassy, F. Provost, and S. Rosset, “ROC confidence bands: An empirical evaluation,” in Proceedings of the 22nd international conference on Machine learning, pp. 537-544, 2005.

A. Tharwat, "Classification assessment methods," Applied computing and informatics, vol. 17, no. 1, pp. 168-192, 2021.

A. M. Carrington et al., "Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 1, pp. 329-341, 2022.

O. Saidani et al., "Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approach," Multimedia Tools and Applications, pp. 1-26, 2024.

K. Takahashi, K. Yamamoto, A. Kuchiba, and T. Koyama, "Confidence interval for micro-averaged F1 and macro-averaged F1 scores," Appl. Intell., vol. 52, no. 5, pp. 4961–4972, 2022, doi: 10.1007/s10489-021-02635-5.

S. M. Abdullah et al., "An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape," arXiv preprint arXiv:2404.16212, 2024.

D. J. Hand, P. Christen, and N. Kirielle, "F*: an interpretable transformation of the F-measure," Mach. Learn., vol. 110, no. 3, pp. 451–456, 2021, doi: 10.1007/s10994-021-05964-1.

T. Gowda, W. You, C. Lignos, and J. May, "Macro-Average: Rare Types Are Important Too," arXiv preprint arXiv:2104.05700, 2021.

G. Pei et al., "Deepfake Generation and Detection: A Benchmark and Survey," arXiv preprint arXiv:2403.17881, 2024.

K. Riehl, M. Neunteufel, and M. Hemberg, "Hierarchical confusion matrix for classification performance evaluation," arXiv preprint arXiv:2306.09461, 2023.