These days, deep learning has gained popularity as a widely used technique for image inpainting. It has the ability to not only recover the texture of a picture and extract abstract information at a high level but also restore semantic representations like human faces. Out of these methods, the use of generative adversarial networks (GANs) with autoencoder as the generator has emerged as a potential paradigm for inpainting. These models utilize the end-to-end inpainting technique to produce visually coherent and distinct image structures and textures. Nevertheless, Generative Adversarial Networks (GANs) frequently encounter issues such as gradient vanishing and model collapse when being trained. To address these challenges, we introduce a novel approach called attribute-driven training of GAN and utilize it for face image inpainting. The proposed work utilizes attributes to stabilize the training process of the generator network. It employs different training keys as training objectives to update the generator network’s parameters. Additionally, it uses a classification module, which helps determine the bestfit key. The discriminator is aided by the loss functions to critique the generated image. Experiments conducted on CelebA [25] datasets demonstrate that the proposed work effectively addresses the issue of artifacts, accomplishes consistent and efficient training, and produces visually plausible images.

The growth of location-based services (LBS) means increased privacy for individuals. Consequently, it re-quires a solid base for user privacy mechanisms. Our study proposes a decentralized method based on Deep Q-Networks (DQN), enhanced by Prioritized Experience Replay (PER), and applies Federated Learning (FL) for a better training process. PER, apart from being informational, aims mainly to provide an effective way to learn while on the other hand, Federated Learning relies on a centralized server to train the model using the replay memory but not at the expense of the privacy of the data during the learning process. After the trained model weights are successfully transferred to client devices, prediction can be performed locally and real-time decisions can be made as positions change. The model incorporates implicit cues presented by app context, frequency of use, and separation factors among others to estimate users' privacy attitude toward location data sharing. The test resourcefulness upholds a claim to the advantages of the augmented DQN model, as shown in the results in which PER and Federated Learning contribute to accelerated convergence and the enhanced ability to absorb information. As a result, there is the utilization of numerous tools that put this power into the hands of individual users in the ongoing development of LBS, hence, a detribalization of the digital space by empowerment.

Fingerprints serve as a primary means of individually identifying individuals. However, employing fingerprints in online mode poses a significant privacy risk, since it is susceptible to several forms of attack. It is plagued by issues related to privacy and security. In response to this, we proposed an innovative approach to convert the original fingerprint into a secure template that may be retained and utilized for authentication purposes. The new templates bear a resemblance to the original human fingerprints and ensure privacy by possessing the characteristic of non-invertibility. This study presented a method for generating highly authentic fingerprint templates that ensure the capacity to revoke and cancel the stolen fingerprint. Throughout the training and testing phase, we utilized the dataset derived from the Vikriti-ID fingerprint. The collection has 25000 distinct fingerprint samples, divided into five classes, with each class containing 5,000 samples. Throughout the testing phase, the comprehensive performance was evaluated based on the matching performance including EER and AUC.

Fingerprint recognition research faces significant challenges due to the limited availability of extensive and publicly available fingerprint databases. Existing databases lack a sufficient number of identities and fingerprint impressions, which hinders progress in areas such as Fingerprint-based access control. To address this challenge, we present Vikriti-ID, a synthetic fingerprint generator capable of generating unique fingerprints with multiple impressions. Using Vikriti-ID, we generated a large database containing 500000 unique fingerprints, each with 10 associated impressions. We then demonstrate the effectiveness of the database generated by Vikriti-ID by evaluating it for imposter-genuine score distribution and EER score. Apart from this we also trained a deep network to check the usability of data. We trained the network inspired from [13], on both Vikriti-ID generated data as well as public data. This generated data achieved an Equal Error Rate(EER) of 0.16%, AUC of 0.89%. This improvement is possible due to the limitations of existing publicly available data sets, which struggle in numbers or multiple impressions.

The safeguarding of biometric data is a crucial aspect of biometric authentication systems, and biometric template protection serves as a means to ensure the confidentiality of such information. In recent times, the utilization of random projection has surfaced as a viable technique for safeguarding biometric templates. This article delves deeper into the security of random projection-based techniques utilized for safeguarding biometric templates. The demonstration showcases that the security of biometric systems is compromised by non-invertibility attacks, as the protected template can be utilized by an attacker to accurately replicate the initial biometric template. A viable approach for executing non-invertibility attacks on systems, based on random projection is additionally furnished by us. The study conducted an experiment on two publicly accessible biometric datasets to demonstrate the vulnerability of random projection-based biometric template security methods. The proposed assault was utilized to recreate the original templates, highlighting the ease with which this could be accomplished. The results of our study emphasize the necessity for innovative methods in safeguarding biometric templates that are impervious to inversion.

This work proposes a novel application of digital twins in the field of biometric security. Biometric systems have become widespread but their use carries risks of privacy invasion attacks due to the sensitive nature of biometric data. To address these concerns, we propose creating biometric clones for digital access and authentication systems. A user's fingerprint can act as a virtual representation or cyberproxy, allowing users to exist in the digital world with a unique, changeable, and privacy-preserving identity. The digital twin or clone fingerprint is generated using deep neural networks combined with a user-specific token/key. This approach allows third parties to process and store the proxy biometrics without putting the user's personal information at risk. We suggest that this approach could provide a safer and more secure alternative to traditional biometric security systems.

Most authentication schemes are centralized or managed by large federated giants. Often data stored in such third parties is highly susceptible to hacks, leaks, cross-matching, selling, and various privacy-invading attacks. Biometric credentials are extremely sensitive as unlike other credentials they cannot be renewed if compromised. This work proposes a blockchain based framework that allows secure, transparent, and privacy-preserving biometric authentication. Instead of storing biometric data in a centralized database they are decentralized and managed using DID and DID documents. It allows a user to posses self-sovereign and revocable pseudo-biometric identities that enables complete control over its biometric identity information, completely anonymous trans-actions, and the right to be forgotten. The pseudo-biometric acts extra protecting by imparting one-way transforms to original biometric and making is absolutely safe to onboard. The scheme is analyzed for performance under various operating scenarios.

The cancelable biometric-based template protection method proposed in this paper addresses security and privacy concerns emerging from the phenomenal usage of biometric systems. Cancelable biometric transforms the original biometric identity of a user to a pseudo-biometric identity that is used for storage and matching purposes. The use of pseudo-identity mitigates privacy risks and allows revocability in case of compromise. This paper proposes a novel template transformation technique named random distance method which not only generates discriminative and privacy preserving revocable pseudo-biometric identities, but also reduces their size by 50%. Extensive experimentation is performed to analyze recognition and protection performance on unimodal and multimodal pseudo-identities generated with various biometric modalities such as face, thermal face, palmprint, palmvein, and fingervein. It is observed that the matching performance obtained with the proposed cancelable templates in the worst-case is closer to the performance achieved in the original domain. Also, multimodal cancelable biometric templates generated with the proposed method are observed for improved performance. Furthermore, the proposed approach is successfully analyzed for non-invertibilty, unlinkability, as well as its resistance for various types of attacks like attacks via record multiplicity, dictionary, false accepts, and brute force.