Hadid, Abdenour

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Hadid, Abdenour
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Sorbonne Centre for Artificial Intelligence (SCAI)
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  • Publication
    Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
    (2022)
    Hadid, Abdenour 
    ;
    Fezza, Sid
    ;
    Ouis, Mohammed
    ;
    Kaddar, Bachir
    ;
    Hamidouche, Wassim
    Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images. The existing works have mainly focused on deepfake in face images and videos. However, we are currently witnessing the emergence of fake satellite images, which can be misleading or even threatening to national security. Consequently, there is an urgent need to develop detection methods capable of distinguishing between real and fake satellite images. To advance the field, in this paper, we explore the suitability of several convolutional neural network (CNN) architectures for fake satellite image detection. Specifically, we benchmark four CNN models by conducting extensive experiments to evaluate their performance and robustness against various image distortions. This work allows the establishment of new baselines and may be useful for the development of CNN-based methods for fake satellite image detection.
      21  3
  • Publication
    Face Sketch Synthesis using Generative Adversarial Networks
    (2022)
    Hadid, Abdenour 
    ;
    Mahfoud, Sami
    ;
    Daamouche, Abdelhamid
    ;
    Bengherabi, Messaoud
    ;
    Boutellaa, Elhocine
    Face Sketch Synthesis is crucial for a wide range of practical applications, including digital entertainment and law enforcement. Recent approaches based on Generative Adversarial Networks (GANs) have shown compelling results in image-to-image translation as well as face photo-sketch synthesis. However, these methods still have considerable limitations as some noise appears in synthesized sketches which leads to poor perceptual quality and poor preserving fidelity. To tackle this issue, in this paper, we propose a Face Sketch Synthesis technique using conditional GAN to generate facial sketches from facial photographs named cGAN-FSS. Our cGAN-FSS framework generates high perceptual quality of face sketch synthesis while maintaining high identity recognition accuracy. Image Quality Assessment metrics and Face Recognition experiments confirm our proposed framework's performs better than the state-of-the-art methods.
      34  10
  • Publication
    Hand-drawn face sketch recognition using rank-level fusion of image quality assessment metrics
    (2022)
    Hadid, Abdenour 
    ;
    Mahfoud, Sami
    ;
    Daamouche, Abdelhamid
    ;
    Bengherabi, Messaoud
    Face Sketch Recognition (FSR) presents a severe challenge to conventional recognition paradigms developed basically to match face photos. This challenge is mainly due to the large texture discrepancy between face sketches, characterized by shape exaggeration, and face photos. In this paper, we propose a training-free synthesized face sketch recognition method based on the rank-level fusion of multiple Image Quality Assessment (IQA) metrics. The advantages of IQA metrics as a recognition engine are combined with the rank level fusion to boost the final recognition accuracy. By integrating multiple IQA metrics into the face sketch recognition framework, the proposed method simultaneously performs face-sketch matching application and evaluates the performance of face sketch synthesis methods. To test the performance of the recognition framework, five synthesized face sketch methods are used to generate sketches from face photos. We use the Borda count approach to fuse four IQA metrics, namely, structured similarity index metric, feature similarity index metric, visual information fidelity and gradient magnitude similarity deviation at the rank-level. Experimental results and comparison with the state-of-the-art methods illustrate the competitiveness of the proposed synthesized face sketch recognition framework.
      66  1
  • Publication
    Kinship recognition from faces using deep learning with imbalanced data
    (2022)
    Hadid, Abdenour 
    ;
    Othmani, Alice
    ;
    Han, Duqing
    ;
    Gao, Xin
    ;
    Ye, Runpeng
    Kinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community due to its potential applications in social media mining and finding missing children. In this work, We propose a novel pattern analysis technique for kinship verification based on a new deep learning-based approach. More specifically, given a pair of face images, we first use Resnet50 to extract deep features from each image. Then, feature distances between each pair of images are computed. Importantly, to overcome the problem of unbalanced data, One Hot Encoding for labels is utilised. The distances finally are fed to a deep neural networks to determine the kinship relation. Extensive experiments are conducted on FIW dataset containing 11 classes of kinship relationships. The experiments showed very promising results and pointed out the importance of balancing the training dataset. Moreover, our approach showed interesting ability of generalization. Results show that our approach performs better than all existing approaches on grandparents-grandchildren type of kinship. To support the principle of open and reproducible research, we are soon making our code publicly available to the research community: github.com/Steven-HDQ/Kinship-Recognition.
      24
  • Publication
    Knowledge-based Deep Learning for Modeling Chaotic Systems
    (2022)
    Elabid, Zakaria
    ;
    Chakraborty, Tanujit 
    ;
    Hadid, Abdenour 
    Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics problems and predicting nonlinear dynamical systems. However, modeling and forecasting the dynamics of chaotic systems remains an open research problem since training deep learning models requires big data, which is not always available in many cases. Such deep learners can be trained from additional information obtained from simulated results and by enforcing the physical laws of the chaotic systems. This paper considers extreme events and their dynamics and proposes elegant models based on deep neural networks, called knowledge-based deep learning (KDL). Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data directly from the dynamics and their differential equations. This knowledge is transferred to model and forecast real-world chaotic events exhibiting extreme behavior. We validate the efficiency of our model by assessing it on three real-world benchmark datasets: El Niño sea surface temperature, San Juan Dengue viral infection, and Bjørnøya daily precipitation, all governed by extreme events' dynamics. Using prior knowledge of extreme events and physics-based loss functions to lead the neural network learning, we ensure physically consistent, generalizable, and accurate forecasting, even in a small data regime. Index Terms-Chaotic systems, long short-term memory, deep learning, extreme event modeling.
      22  2
  • Publication
    PARNN: A Probabilistic Autoregressive Neural Network Framework for Accurate Forecasting
    (2022)
    Hadid, Abdenour 
    ;
    Chakraborty, Tanujit 
    ;
    Panja, Madhurima
    ;
    Kumar, Uttam
    Forecasting time series data represents an emerging field of research in data science and knowledge discovery with vast applications ranging from stock price and energy demand prediction to the early prediction of epidemics. Numerous statistical and machine learning methods have been proposed in the last five decades with the demand for high-quality and reliable forecasts. However, in real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable. Therefore, hybrid solutions are needed to bridge the gap between classical forecasting methods and modern neural network models. In this context, we introduce a Probabilistic AutoRegressive Neural Network (PARNN) model that can handle a wide variety of complex time series data (e.g., nonlinearity, non-seasonal, long-range dependence, and non-stationarity). The proposed PARNN model is built by creating a fusion of an integrated moving average and autoregressive neural network to preserve the explainability, scalability, and "white-boxlike" prediction behavior of the individuals. Sufficient conditions for asymptotic stationarity and geometric ergodicity are obtained by considering the asymptotic behavior of the associated Markov chain. Unlike advanced deep learning tools, the uncertainty quantification of the PARNN model based on prediction intervals is obtained. During computational experiments, PARNN outperforms standard statistical, machine learning, and deep learning models (e.g., Transformers, NBeats, DeepAR, etc.) on a diverse collection of real-world datasets from macroeconomics, tourism, energy, epidemiology, and others for short-term, medium-term, and long-term forecasting. Multiple comparisons with the best method are carried out to showcase the superiority of the proposal in comparison with the state-ofthe-art forecasters over different forecast horizons.
      55  2
  • Publication
    Vehicular Environment Identification Based on Channel State Information and Deep Learning
    (2022)
    Hadid, Abdenour 
    ;
    Ribouh, Soheyb
    ;
    Sadli, Rahmad
    ;
    Elhillali, Yassin
    ;
    Rivenq, Atika
    This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the environment type in which the vehicle is driving, without any need to implement specific sensors such as cameras or radars. We consider environment identification as a classification problem, and propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI is used as the input feature to train the model. To perform the identification process, the model is targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The proposed model is extensively evaluated, showing that it can reliably recognize the surrounding environment with high accuracy (96.48%). Our model is compared to related approaches and state-ofthe-art classification architectures. The experiments show that our proposed model yields favorable performance compared to all other considered methods.
      64  17
  • Publication
    W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
    (2022)
    Sasal, Lena
    ;
    Chakraborty, Tanujit 
    ;
    Hadid, Abdenour 
    Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others. Among several merits of transformers, the ability to capture long-range temporal dependencies and interactions is desirable for time series forecasting, leading to its progress in various time series applications. In this paper, we build a transformer model for non-stationary time series. The problem is challenging yet crucially important. We present a novel framework for univariate time series representation learning based on the wavelet-based transformer encoder architecture and call it W-Transformer. The proposed W-Transformers utilize a maximal overlap discrete wavelet transformation (MODWT) to the time series data and build local transformers on the decomposed datasets to vividly capture the nonstationarity and long-range nonlinear dependencies in the time series. Evaluating our framework on several publicly available benchmark time series datasets from various domains and with diverse characteristics, we demonstrate that it performs, on average, significantly better than the baseline forecasters for short-term and long-term forecasting, even for datasets that consist of only a few hundred training samples.
      32  12