Fingerprint Shell with impregnable features

On the methodology of fingerprint template protection schemes conception : meditations on the reliability

Among the most major potential attacks against fingerprint authentication systems are those that target the stored reference templates. These threats are extremely damaging as they can lead to the invasion of user privacy. The countermeasures to secure fingerprint templates are therefore an indisputable necessity. In literature, although there are so many approaches that address this kind of vulnerability, it turns out to be very difficult to generalize their uses. Given that each system has its own particularities, going from the fingerprint trait acquisition to the matching process, the majority of protection schemes, that are proposed as generic solutions, are not sufficiently mature for large-scale deployment. Consequently, we believe that the methodology of fingerprint template protection schemes conception should be oriented to build specific protection schemes for every unprotected system, which will provide the best compromise between performance and security compared to any generic protection solution. By adopting this methodology, we propose in this paper a new protection scheme for fingerprint templates that is well adapted to a well-known existing unprotected fingerprint minutia system. Our experimental results, obtained using standard benchmarks such as FVC 2002 DB1 and DB2, have proven that the proposed technique meets the requirements of revocability, unlinkability, non-invertibility, and high recognition accuracy.

Image Forgery Detection Using Deeplearning by Recompressing Images

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning-based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%.

Optimized Authentication System with High Security and Privacy

Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique.