“…Another technique, where MC-CNN model [ 169 ] is used illustrates promising performance for counter-measuring all the three types of attacks. The highest HTER of 7.89% is achieved by [ 146 ] in case of photo attacks. Fig.…”
Section: Analysis and Discussionmentioning
confidence: 99%
“…Finally, testing is carried out and final classification score is computed for discriminating live and fake face images. Arora et al [ 146 ] used convolutional auto-encoders to diminish the dimensionality of images. The encoder weights are loaded to another network consisting of Flatten layer and FC layer.…”
Section: State-of-the Art Face Pad Mechanismsmentioning
The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.
“…Another technique, where MC-CNN model [ 169 ] is used illustrates promising performance for counter-measuring all the three types of attacks. The highest HTER of 7.89% is achieved by [ 146 ] in case of photo attacks. Fig.…”
Section: Analysis and Discussionmentioning
confidence: 99%
“…Finally, testing is carried out and final classification score is computed for discriminating live and fake face images. Arora et al [ 146 ] used convolutional auto-encoders to diminish the dimensionality of images. The encoder weights are loaded to another network consisting of Flatten layer and FC layer.…”
Section: State-of-the Art Face Pad Mechanismsmentioning
The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.
“…It proposes CDCN++, which contains a CDC backbone search and multi-scale attention model with fusion. The article [18], introduces a convolutional autoencoder to minimize the dimensionality of images. Moreover, classification and feature extraction of spoofed and natural is done using pre-trained weights.…”
Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems. This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure. First, this proposed method is tested with the Cross-ethnicity Face Anti-spoofing (CASIA), Fetal alcohol spectrum disorders (FASD) dataset. This database has three models of attacks: distorted photographs in printed form, photographs with removed eyes portion, and video attacks. The images are taken with three different quality cameras: low, average, and high-quality real and spoofed images. An extensive experimental study was performed with CASIA-FASD, 3 Diagnostic Machine Aid-Digital (DMAD) dataset that proved higher results when compared to existing algorithms.
“…However, for comparing the proposed algorithm with similar works, we tried to simulate, once again, some of the existing algorithms for the conditions close to the test conditions of that paper. A method based on pre-training has been introduced in [37] for face recognition applications. In this approach, the image features are extracted first and the dimensions are reduced.…”
Section: Accuracy (%)mentioning
confidence: 99%
“…In this approach, the image features are extracted first and the dimensions are reduced. We applied the method presented in [37] to the models used in this paper, called the technique "SDF" for short, and compared it with the results of our work in Table 5. Another robustification technique based on the detection and mitigation of adversarial attacks has been proposed by Goswami et al [38] for boosting the system robustness in face recognition tasks.…”
Despite their many advantages and positive features, the deep neural networks are extremely vulnerable against adversarial attacks. This drawback has substantially reduced the adversarial accuracy of the visual object detectors. To make these object detectors robust to adversarial attacks, a new Gabor filter-based method has been proposed in this paper. This method has then been applied on the YOLOv3 with different backbones, the SSD with different input sizes and on the FRCNN; and thus, six robust object detector models have been presented. In order to evaluate the efficacy of the models, they have been subjected to adversarial training via three types of targeted attacks (TOG-fabrication, TOG-vanishing, and TOG-mislabeling) and three types of untargeted random attacks (DAG, RAP, and UEA). The best average accuracy (49.6%) was achieved by the YOLOv3-d model, and for the PASCAL VOC dataset. This is far superior to the best performance and accuracy and obtained in previous works (25.4%). Empirical results show that, while the presented approach improves the adversarial accuracy of the object detector models, it does not affect the performance of these models on clean data.
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