Fig 2 - available via license: Creative Commons Attribution 4.0 International
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General block diagram of the proposed method: we train a mapping network from facial templates (input) to the intermediate latent space W of GNeRF model. The mapped latent codes along with camera parameters are fed to the GNeRF generator and renderer network (fixed) to generate face image from desired view. Sample outputs of our model (frontal image, view-grid, and 3D face reconstruction) for face reconstruction from B. Obama's facial template are depicted.
Source publication
In this paper, we comprehensively evaluate the vulnerability of state-of-the-art face recognition systems to template inversion attacks using 3D face reconstruction. We propose a new method (called GaFaR) to reconstruct 3D faces from facial templates using a pretrained geometry-aware face generation network, and train a mapping from facial template...
Contexts in source publication
Context 1
... facial templates to the GNeRF intermediate latent space (supervised learning). At the inference stage, we have the 3D reconstructed face and can generate a face image from any arbitrary pose. Thus, we apply optimization on the camera parameters to generate face images with a pose that can increase the success attack rate against the FR system. Fig. 2 illustrates the general block diagram of our proposed template inversion ...
Context 2
... [54] to find the histograms of the pose of original and reconstructed face images in attack 1 against 17 ArcFace on the MOBIO and LFW datasets. As the histograms in this figure show, most of the pose-optimized reconstructed face images have a small variation around the frontal pose. This observation is also consistent with our ablation study in Fig. 11 and Fig. 12, where we see that the intervals of Φ and Θ are not required to be very large. In addition, Fig. 13 also shows that the pose of reconstructed face images does not have the same distribution as that of the original face images. This demonstrates that our camera parameter optimization methods (CO or GS) do not try to find the same pose ...
Citations
This study investigates sentiment analysis methodologies within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining), aiming to discern the efficacy of various algorithms in sentiment classification tasks. The research uses a structured approach to evaluate SVM, NBC, DT, and K-NN algorithms with the SMOTE oversampling technique, uncovering distinct performance metrics and limitations. Results indicate SVM achieving 59.88% accuracy, NBC at 59.25%, DT with 52.09%, and K-NN obtaining 54.80%, highlighting the differential precision, recall, and f-measure. Additionally, content analysis identifies pertinent themes such as Biometric security, Cloud storage, and Emotion Analysis, enriching sentiment dynamics comprehension. The toxicity scores of analyzed videos reveal nuanced sentiment nuances, with the first video exhibiting Toxicity: 0.13227 and the second scoring Toxicity: 0.12794. This study underscores the significance of informed algorithm selection and evaluation methodologies within CRISP-DM, fostering optimized sentiment analysis outcomes while acknowledging diverse topical nuances.
Face recognition systems use the templates (extracted from users’ face images) stored in the system’s database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images reconstructed from those templates. In this paper, we propose a framework to evaluate the vulnerability of face recognition systems to template inversion attacks. We build our framework upon a real-world scenario and measure the vulnerability of the system in terms of the adversary’s success attack rate in entering the system using the reconstructed face images. We propose a face reconstruction network based on a new block called “enhanced deconvolution using cascaded convolution and skip connections" (shortly,
DSCasConv
), and train it with a multi-term loss function. We use our framework to evaluate the vulnerability of state-of-the-art face recognition models, with different network structures and loss functions (in total 31 models), on the MOBIO, LFW, and AgeDB face datasets. Our experiments show that the reconstructed face images can be used to enter the system, which threatens the system’s security. Additionally, the reconstructed face images may reveal important information about each user’s identity, such as race, gender, and age, and hence jeopardize the users’ privacy.
