Touradj Ebrahimi

Yuhang Lu, Touradj Ebrahimi

Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance of such detectors is often assessed on related benchmarks that hardly reflect real-world situations. For example, the impact of various image and video processing operations and typical workflow distortions on detection accuracy has not been systematically measured. In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings. To the best of our acknowledgment, it is the first systematic assessment approach for deepfake detectors that not only reports the general performance under real-world conditions but also quantitatively measures their robustness toward different processing operations. To demonstrate the effectiveness and usage of the framework, extensive experiments and detailed analysis of three popular deepfake detection methods are further presented in this paper. In addition, a stochastic degradation-based data augmentation method driven by realistic processing operations is designed, which significantly improves the robustness of deepfake detectors.

Zewei Xu, Yuhang Lu, Touradj Ebrahimi

Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their "black-box" nature. In recent years, studies have been carried out to give an interpretation of the decision of a deep FR system. However, the affinity between the input facial image and the extracted deep features has not been explored. This paper contributes to the problem of explainable face recognition by first conceiving a face reconstruction-based explanation module, which reveals the correspondence between the deep feature and the facial regions. To further interpret the decision of an FR model, a novel visual saliency explanation algorithm has been proposed. It provides insightful explanation by producing visual saliency maps that represent similar and dissimilar regions between input faces. A detailed analysis has been presented for the generated visual explanation to show the effectiveness of the proposed method.

Yuhang Lu, Touradj Ebrahimi

Deep convolutional neural networks have achieved exceptional results on multiple detection and recognition tasks. However, the performance of such detectors are often evaluated in public benchmarks under constrained and non-realistic situations. The impact of conventional distortions and processing operations found in imaging workflows such as compression, noise, and enhancement are not sufficiently studied. Currently, only a few researches have been done to improve the detector robustness to unseen perturbations. This paper proposes a more effective data augmentation scheme based on real-world image degradation process. This novel technique is deployed for deepfake detection tasks and has been evaluated by a more realistic assessment framework. Extensive experiments show that the proposed data augmentation scheme improves generalization ability to unpredictable data distortions and unseen datasets.

Yuhang Lu, Touradj Ebrahimi

The detection of digital face manipulation in video has attracted extensive attention due to the increased risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been developed and have shown impressive results. However, the performance of these detectors is often evaluated using benchmarks that hardly reflect real-world situations. For example, the impact of various video processing operations on detection accuracy has not been systematically assessed. To address this gap, this paper first analyzes numerous real-world influencing factors and typical video processing operations. Then, a more systematic assessment methodology is proposed, which allows for a quantitative evaluation of a detector's robustness under the influence of different processing operations. Moreover, substantial experiments have been carried out on three popular deepfake detectors, which give detailed analyses on the impact of each operation and bring insights to foster future research.

Yuhang Lu, Touradj Ebrahimi

Despite the significant progress in face recognition in the past years, they are often treated as "black boxes" and have been criticized for lacking explainability. It becomes increasingly important to understand the characteristics and decisions of deep face recognition systems to make them more acceptable to the public. Explainable face recognition (XFR) refers to the problem of interpreting why the recognition model matches a probe face with one identity over others. Recent studies have explored use of visual saliency maps as an explanation, but they often lack a deeper analysis in the context of face recognition. This paper starts by proposing a rigorous definition of explainable face recognition (XFR) which focuses on the decision-making process of the deep recognition model. Following the new definition, a similarity-based RISE algorithm (S-RISE) is then introduced to produce high-quality visual saliency maps. Furthermore, an evaluation approach is proposed to systematically validate the reliability and accuracy of general visual saliency-based XFR methods.

Yuhang Lu, Touradj Ebrahimi

Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems. It aims at matching a low-resolution probe image with high-resolution gallery images registered in a database. Existing methods mainly leverage prior information from high-resolution images by either reconstructing facial details with super-resolution techniques or learning a unified feature space. To address this challenge, this paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image. A cross-resolution knowledge distillation paradigm is first employed as the learning framework. Then, an identity-preserving network, WaveResNet, and a wavelet similarity loss are designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic low-resolution training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.

Yuhang Lu, Ruizhi Luo, Touradj Ebrahimi

Deep convolutional neural networks have shown remarkable results on multiple detection tasks. Despite the significant progress, the performance of such detectors are often assessed in public benchmarks under non-realistic conditions. Specifically, impact of conventional distortions and processing operations such as compression, noise, and enhancement are not sufficiently studied. This paper proposes a rigorous framework to assess performance of learning-based detectors in more realistic situations. An illustrative example is shown under deepfake detection context. Inspired by the assessment results, a data augmentation strategy based on natural image degradation process is designed, which significantly improves the generalization ability of two deepfake detectors.

Yuhang Lu, Zewei Xu, Touradj Ebrahimi

Over recent years, deep convolutional neural networks have significantly advanced the field of face recognition techniques for both verification and identification purposes. Despite the impressive accuracy, these neural networks are often criticized for lacking explainability. There is a growing demand for understanding the decision-making process of AI-based face recognition systems. Some studies have investigated the use of visual saliency maps as explanations, but they have predominantly focused on the specific face verification case. The discussion on more general face recognition scenarios and the corresponding evaluation methodology for these explanations have long been absent in current research. Therefore, this manuscript conceives a comprehensive explanation framework for face recognition tasks. Firstly, an exhaustive definition of visual saliency map-based explanations for AI-based face recognition systems is provided, taking into account the two most common recognition situations individually, i.e., face verification and identification. Secondly, a new model-agnostic explanation method named CorrRISE is proposed to produce saliency maps, which reveal both the similar and dissimilar regions between any given face images. Subsequently, the explanation framework conceives a new evaluation methodology that offers quantitative measurement and comparison of the performance of general visual saliency explanation methods in face recognition. Consequently, extensive experiments are carried out on multiple verification and identification scenarios. The results showcase that CorrRISE generates insightful saliency maps and demonstrates superior performance, particularly in similarity maps in comparison with the state-of-the-art explanation approaches.

Michela Testolina, Mohsen Jenadeleh, Shima Mohammadi et al.

Advances in image compression, storage, and display technologies have made high-quality images and videos widely accessible. At this level of quality, distinguishing between compressed and original content becomes difficult, highlighting the need for assessment methodologies that are sensitive to even the smallest visual quality differences. Conventional subjective visual quality assessments often use absolute category rating scales, ranging from ``excellent'' to ``bad''. While suitable for evaluating more pronounced distortions, these scales are inadequate for detecting subtle visual differences. The JPEG standardization project AIC is currently developing a subjective image quality assessment methodology for high-fidelity images. This paper presents the proposed assessment methods, a dataset of high-quality compressed images, and their corresponding crowdsourced visual quality ratings. It also outlines a data analysis approach that reconstructs quality scale values in just noticeable difference (JND) units. The assessment method uses boosting techniques on visual stimuli to help observers detect compression artifacts more clearly. This is followed by a rescaling process that adjusts the boosted quality values back to the original perceptual scale. This reconstruction yields a fine-grained, high-precision quality scale in JND units, providing more informative results for practical applications. The dataset and code to reproduce the results will be available at https://github.com/jpeg-aic/dataset-BTC-PTC-24.

Mohsen Jenadeleh, Jon Sneyers, Davi Lazzarotto et al.

High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual differences are subtle. To address this gap, we introduce AIC-HDR2025, the first such HDR dataset, comprising 100 test images generated from five HDR sources, each compressed using four codecs at five compression levels. It covers the high-fidelity range, from visible distortions to compression levels below the visually lossless threshold. A subjective study was conducted using the JPEG AIC-3 test methodology, combining plain and boosted triplet comparisons. In total, 34,560 ratings were collected from 151 participants across four fully controlled labs. The results confirm that AIC-3 enables precise HDR quality estimation, with 95\% confidence intervals averaging a width of 0.27 at 1 JND. In addition, several recently proposed objective metrics were evaluated based on their correlation with subjective ratings. The dataset is publicly available.

Yuhang Lu, Touradj Ebrahimi

Over the past years, image generation and manipulation have achieved remarkable progress due to the rapid development of generative AI based on deep learning. Recent studies have devoted significant efforts to address the problem of face image manipulation caused by deepfake techniques. However, the problem of detecting purely synthesized face images has been explored to a lesser extent. In particular, the recent popular Diffusion Models (DMs) have shown remarkable success in image synthesis. Existing detectors struggle to generalize between synthesized images created by different generative models. In this work, a comprehensive benchmark including human face images produced by Generative Adversarial Networks (GANs) and a variety of DMs has been established to evaluate both the generalization ability and robustness of state-of-the-art detectors. Then, the forgery traces introduced by different generative models have been analyzed in the frequency domain to draw various insights. The paper further demonstrates that a detector trained with frequency representation can generalize well to other unseen generative models.

Yuhang Lu, Zewei Xu, Touradj Ebrahimi

Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications widely spread in people's lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such systems. Existing studies relying on various mechanisms have investigated the usage of saliency maps as an explanation approach, but suffer from different limitations. This paper first explores the spatial relationship between face image and its deep representation via gradient backpropagation. Then a new explanation approach FGGB has been conceived, which provides precise and insightful similarity and dissimilarity saliency maps to explain the "Accept" and "Reject" decision of an FR system. Extensive visual presentation and quantitative measurement have shown that FGGB achieves superior performance in both similarity and dissimilarity maps when compared to current state-of-the-art explainable face verification approaches.

Nikolay Stanishev, Yuhang Lu, Touradj Ebrahimi

Pose-invariant face recognition has become a challenging problem for modern AI-based face recognition systems. It aims at matching a profile face captured in the wild with a frontal face registered in a database. Existing methods perform face frontalization via either generative models or learning a pose robust feature representation. In this paper, a new method is presented to perform face frontalization and recognition within the feature space. First, a novel feature space pose frontalization module (FSPFM) is proposed to transform profile images with arbitrary angles into frontal counterparts. Second, a new training paradigm is proposed to maximize the potential of FSPFM and boost its performance. The latter consists of a pre-training and an attention-guided fine-tuning stage. Moreover, extensive experiments have been conducted on five popular face recognition benchmarks. Results show that not only our method outperforms the state-of-the-art in the pose-invariant face recognition task but also maintains superior performance in other standard scenarios.

Yuhang Lu, Zewei Xu, Touradj Ebrahimi

In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. There has been an increasing demand for understanding the decision-making process of deep face recognition systems. Recent studies have investigated the usage of visual saliency maps as an explanation, but they often lack a discussion and analysis in the context of face recognition. This paper concentrates on explainable face verification tasks and conceives a new explanation framework. Firstly, a definition of the saliency-based explanation method is provided, which focuses on the decisions made by the deep FR model. Secondly, a new model-agnostic explanation method named CorrRISE is proposed to produce saliency maps, which reveal both the similar and dissimilar regions of any given pair of face images. Then, an evaluation methodology is designed to measure the performance of general visual saliency explanation methods in face verification. Finally, substantial visual and quantitative results have shown that the proposed CorrRISE method demonstrates promising results in comparison with other state-of-the-art explainable face verification approaches.

Yuhang Lu, Evgeniy Upenik, Touradj Ebrahimi

Deepfakes are becoming increasingly popular in both good faith applications such as in entertainment and maliciously intended manipulations such as in image and video forgery. Primarily motivated by the latter, a large number of deepfake detectors have been proposed recently in order to identify such content. While the performance of such detectors still need further improvements, they are often assessed in simple if not trivial scenarios. In particular, the impact of benign processing operations such as transcoding, denoising, resizing and enhancement are not sufficiently studied. This paper proposes a more rigorous and systematic framework to assess the performance of deepfake detectors in more realistic situations. It quantitatively measures how and to which extent each benign processing approach impacts a state-of-the-art deepfake detection method. By illustrating it in a popular deepfake detector, our benchmark proposes a framework to assess robustness of detectors and provides valuable insights to design more efficient deepfake detectors.

Romain Artru, Alexandre Gouaillard, Touradj Ebrahimi

Digital Watermarking is an extremely wide aspect of information security, either by its applications, by its properties, or by its designs. In particular, a lot of research has been made about video watermarking and it can make it quite difficult to put into perspective the various schemes possible in order to implement a watermarking process for a given application. This paper presents an in-depth overview of the current video watermarking technologies and how they each respond to certain criteria that may be imposed by the aimed application. The goal being in first place to be able to define the desired equilibrium point between invisibility, robustness and efficiency for an application. Then, given this balance, being able to deduce the best location of the information embedding as well as the method used to embed it. The equilibrium point is to be found using the needed properties of the watermark and by studying the threat model that the scheme will have to face. The location describes whether the extra information should be added to the metadata of the video, to its frames or to specific regions of its frames. Finally, the method to embed the watermark refers to the insertion domain and its coefficients to be altered in order to insert the wanted information.

Alessandro Artusi, Thomas Richter, Touradj Ebrahimi et al.

In this lecture note, we describe high dynamic range (HDR) imaging systems; such systems are able to represent luminances of much larger brightness and, typically, also a larger range of colors than conventional standard dynamic range (SDR) imaging systems. The larger luminance range greatly improve the overall quality of visual content, making it appears much more realistic and appealing to observers. HDR is one of the key technologies of the future imaging pipeline, which will change the way the digital visual content is represented and manipulated today.