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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohammed Berrahal
Mohammed First University
Electrical & Electronics Engineering

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Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques Mohammed Berrahal; Mostafa Azizi
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp972-979

Abstract

The advancements in artificial intelligence research, particularly in computer vision, have led to the development of previously unimaginable applications, such as generating new contents based on text description. In our work we focused on the text-to-image synthesis applications (TIS) field, to transform descriptive sentences into a real image. To tackle this issue, we use unsupervised deep learning networks that can generate high quality images from text descriptions, provided by eyewitnesses to assist law enforcement in their investigations, for the purpose of generating probable human faces. We analyzed a number of existing approaches and chose the best one. Deep fusion generative adversarial networks (DF-GAN) is the network that performs better than its peers, at multiple levels, like the generated image quality or the respect of the giving descriptive text. Our model is trained on the CelebA dataset and text descriptions (generated by our algorithm using existing attributes in the dataset). The obtained results from our implementation show that the learned generative model makes excellent quantitative and visual performances, the model is capable of generating realistic and diverse samples for human faces and create a complete portrait with respect of given text description.
Augmented binary multi-labeled CNN for practical facial attribute classification Mohammed Berrahal; Mostafa Azizi
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 2: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i2.pp973-979

Abstract

Both human face recognition and generation by machines are currently an active area of computer vision, drawing curiosity of researchers, capable of performing amazing image analysis, and producing applications in multiple domains. In this paper, we propose a new approach for face attributes classification (FAC) taking advantage from both binary classification and data augmentation. With binary classification we can reach high prediction scores, while augmented data prevent overfitting and overcome the lack of data for sketched photos. Our approach, named Augmented binary multilabel CNN (ABM-CNN), consists of three steps: i) splitting data; ii) transformed-it to sketch (simplification process); iii) train separately each attribute with two convolutional neural networks; the whole process includes two networks: the first (resp. the second) one is to predict attributes on real images (resp. sketches) as inputs. Through experimentation, we figure out that some attributes give high prediction rates with sketches rather than with real images. On the other hand, we build a new face dataset, more consistent and complete, by generating images using Style-GAN model, to which we apply our method for extracting face attributes. As results, our proposal demonstrates more performances compared to those of related works.
Investigating the effectiveness of deep learning approaches for deep fake detection Mohammed Berrahal; Mohammed Boukabous; Mimoun Yandouzi; Mounir Grari; Idriss Idrissi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6221

Abstract

As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This paper aims to provide an in-depth discussion about the challenge of generated fake face image detection. We explain the different datasets and the various proposed deep learning models for fake face image detection. The models used were trained on a large dataset of real data from CelebA-HQ and fake data from a trained generative adversarial network (GAN) based generator. All testing models achieved high accuracy in detecting the fake images, especially residual neural network (ResNet50) which performed the best among with an accuracy of 99.43%.
Co-Authors Idriss Idrissi Mimoun Yandouzi Mohammed Boukabous Mostafa Azizi Mounir Grari