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%.

Fingerprint authentication systems generally save data extracted from fingerprint as minutiae template in the database. However, it is often found that databases can be attacked and compromised by the adversary. So if minutiae points of a user are leaked, the fingerprint can be generated from them. The fingerprint cannot be changed as the finger is a part of the human body. Hence, securing information extracted from the fingerprint is required. In this study, the authors propose a highly secure technique that uses location information of the minutia points to construct a highly secured template for a user. For every minutia point, secured modified location is generated by using information of its neighbouring minutiae and a key‐set. They have achieved 2, 1, and 3.1% equal error rate values for FVC2002 DB1, DB2, and DB3 fingerprint databases, respectively, under the same key scenario. Analysis done for several attacks shows that the proposed technique is very robust and secure. The experimental results are extremely encouraging and they demonstrate the efficiency of the proposed technique.

Human recognition with biometrics is a rapidly emerging area of computer vision. Compared to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the ear has recently received considerable research attention. The ear recognition system accepts 2D or 3D images as input. Since pose, illumination, and scale all affect 2D ear images, it is evident that they all impact recognition performance; therefore, 3D ear images are employed to address these issues. The geometric shapes of 3D ears are utilized as rich features to improve recognition accuracy. We present recent advances in several areas relevant to 3D ear recognition and provide directions for future research. To the best of our knowledge, no comprehensive review has been conducted on using 3D ear images in human recognition. This review focuses on three primary categories of 3D ear recognition techniques: (1) registration-based recognition, (2) local and global feature-based recognition, and (3) a combination of (1) and (2). Based on the above categorization and publicly available 3D ear datasets, this article reviews existing 3D ear recognition techniques.

Fingerprint matching is one of the extensively used tools to authenticate users. It has been accepted by governments of various countries to authenticate their citizens. The common practice in fingerprint based system is to store minutiae points of fingerprints as the user template. The minutiae points information can be access through an attack by an adversary and the original fingerprint of users can be obtained using this information. Unlike passwords, a compromised fingerprint is irreparable and there is a necessity to protect user data. In this paper, a technique called Polynomial Vault is proposed to secure fingerprint information. The fingerprint features and a unique user key-set are used to generate a secure template for a user by using a non-invertible transformation. This obtained template is used to authenticate the user. In case, a user template is compromised, the proposed technique provides a way to construct a new template by changing the user key-set values. To demonstrate the efficiency and the robustness of the proposed technique, a thorough experimental analysis is being conducted using IIT Kanpur, FVC2004 (DB1 and DB2), FVC2002 (DB1, DB2, DB3, and DB4), and FVC2000 (DB1 and DB2) databases.