Image Processing Projects

ABSTRACT Biometric access control systems are becoming more commonplace in society. However, these systems are susceptible to replay attacks. During a replay attack, an attacker can capture packets of data that represents an individual’s biometric. The attacker can then replay the data and gain unauthorized access into the system. Traditional password based systems have the ability to use a one-time password scheme. This allows for a unique password to authenticate an individual and it is then disposed. Any captured password will not be effective. Traditional biometric systems use a single feature extraction method to represent an individual, making captured data harder to change than a password. Contact:
 +91-9008001602
 080-40969981

ABSTRACT Traditional password conversion scheme for user authentication is to transform the passwords into hash values. These hash-based password schemes are comparatively simple and fast because those are based on text and famed cryptography. However, those can be exposed to cyber-attacks utilizing password by cracking tool or hash-cracking online sites. Attackers can thoroughly figure out an original password from hash value when that is relatively simple and plain. As a result, many hacking accidents have been happened predominantly in systems adopting those hash-based schemes. In this work, we suggest enhanced password processing scheme based on image using visual cryptography (VC). Different from the traditional scheme based on hash and text, our scheme transforms a user ID of text type to two images encrypted by VC.Contact:
 +91-9008001602
 080-40969981

ABSTRACT Visual cryptography (VC) is a variant form of secret sharing. In general threshold setting, the k-out-of-n VC allows that, in a set of n participants, any k can recover and reconstruct the secret by stacking their shares. Recently, the notion of multiple-secret VC has been introduced to embed multiple secrets. Region incrementing visual cryptography (RIVC) is referred to as a new type of multi-secret VC. RIVC defines s layers and takes s secrets, and then embeds each secret into each layer. The layers are defined by the number of participants; for example, let two secrets and two layers be S2; S3 and L2;L3 in 2-out-of-3 RIVC, where any two participants in L2 can recover S2 and three in L3 can recover S2; S3. However, there is another multi-secret VC, called fully incrementing visual cryptography (FIVC), which also has the layers, but only one secret Si will reveal in one layer Li.Contact:
 +91-9008001602
 080-40969981

ABSTRACTWe address the problem of shoulder-surfing attacks on authentication schemes by proposing IllusionPIN (IPIN), a PIN-based authentication method that operates on touchscreen devices. IPIN uses the technique of hybrid images to blend two keypads with different digit orderings in such a way, that the user who is close to the device is seeing one keypad to enter her PIN, while the attacker who is looking at the device from a bigger distance is seeing only the other keypad. The user’s keypad is shuffled in every authentication attempt since the attacker may memorize the spatial arrangement of the pressed digits.Contact:
 +91-9008001602
 080-40969981

ABSTRACTReversible data hiding (RDH) in encrypted images has attained more attention recently in research community. Privacy protection of additional data as well as cover media makes it attractive for applications in medical imaging, cloud storage, forensics etc. In this paper, a new method for reversible data hiding in encrypted images (RDH-EI), is proposed. Our method adopts the approach of reserving sufficient space for the additional data before encrypting the cover image. First we identify suitable blocks for hiding data from various parts of the image. Before encrypting the image, one or more LSB-planes of these blocks are backed-up into remaining parts of the image using a high-performing traditional RDH method that works on unencrypted images. After encrypting the image, those LSBplanes are used to hide additional data. Recovery of original cover image and error-free extraction of additional data is guaranteed always.Contact:
 +91-9008001602
 080-40969981

ABSTRACT We address the problem of shoulder-surfing attacks on authentication schemes by proposing IllusionPIN (IPIN), a PIN-based authentication method that operates on touchscreen devices. IPIN uses the technique of hybrid images to blend two keypads with different digit orderings in such a way, that the user who is close to the device is seeing one keypad to enter her PIN, while the attacker who is looking at the device from a bigger distance is seeing only the other keypad. The user’s keypad is shuffled in every authentication attempt since the attacker may memorize the spatial arrangement of the pressed digits.Contact:
 +91-9008001602
 080-40969981

ABSTRACTThis paper presents an online highly accurate system for automatic number plate recognition (ANPR) that can be used as a basis for many real-world ITS applications. The system is designed to deal with unclear vehicle plates, variations in weather and lighting conditions, different traffic situations, and high-speed vehicles. This paper addresses various issues by presenting proper hardware platforms along with real-time, robust, and innovative algorithms. We have collected huge and highly inclusive data sets of Persian license plates for evaluations, comparisons, and improvement of various involved algorithms. Contact:
 +91-9008001602
 080-40969981

ABSTRACT Human action recognition has been well explored in applications of computer vision. Many successful action recognition methods have shown that action knowledge can be effectively learned from motion videos or still images. For the same action, the appropriate action knowledge learned from different types of media, e.g., videos or images, may be related. However, less effort has been made to improve the performance of action recognition in videos by adapting the action knowledge conveyed from images to videos. Most of the existing video action recognition methods suffer from the problem of lacking sufficient labeled training videos. In such cases, over-fitting would be a potential problem and the performance of action recognition is restrained. In this paper, we propose an adaptation method to enhance action recognition in videos by adapting knowledge from images.Contact:
 +91-9008001602
 080-40969981