Reliably signalling a startling husbandry event improves welfare of zoo-housed capuchins (Sapajus apella)

Animals kept in captivity are reliant on humans for their care and welfare. Enclosure design, and choice of group mates as well as routine husbandry events such as feeding, cleaning, and health care are in the hands of human keepers. It is therefore important to understand how external human-related husbandry events affect daily behaviour routines for animals, to help promote good welfare. Predictability (or lack thereof) of these routines can have profound effects on behaviours of captive animals. This study investigates whether providing a reliable predictable signal indicating entry into indoor brown capuchin (Sapajus apella) enclosures can increase welfare. All day focal follows of 12 zoo-housed capuchins were performed, recording behaviour in relation to husbandry events. The Baseline data show that unreliable sounds of door openings and closings outside the enclosure increase anxiety-related behaviours such as self-scratching, vigilance and jerky motions, and that the capuchins were startled by keepers entering the enclosure. A reliable signal (knocking) was subsequently introduced before enclosure entry and the monkeys given two weeks to associate the signal prior to Treatment condition data collection. The results indicate that the anxiety-related behaviours were reduced in the Treatment condition compared to Baseline frequencies. We conclude that making certain husbandry events reliable and predictable through the introduction of a unique signal can have a significant positive impact on the welfare of animals. Such an approach is not time consuming and costs nothing to implement, yet can result in significant advancements in animal welfare that can be implemented in a wide range of captive settings

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center– of the University of Florence) for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression

Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan

Manipulation of digital images is not considered a new thing nowadays. For as long as cameras have existed, photographers have been staged and images have been forged and passed off for more nefarious purposes. Region duplication is regarded as an efficient and simple operation for image forgeries, where a part of the image itself is copied and pasted into a different part of the same image grid. The detection of duplicated regions can be a challenging task in digital image forensic (DIF) when images are used as evidence to influence the judgment, such as in court of law. Existing methods have been developed in the literature to reveal duplicated regions. These methods are classified into block-based and key point-based methods. Most prior block based methods rely on exhaustive block matching on image contents and suffer from their inability to localize this type of forgery when the duplicated regions have gone through some geometric transformation operations and post-processing operations. In this research, we propose three novel approaches for detecting duplicate regions in forged images that are robust to common geometric transformations and post processing operations. In the first approach, we propose a novel method for detecting uniform and non-uniform duplicated regions with small size in forged images that is robust to geometric transformation operations such as rotation and scaling. The proposed method have adopted statistical region merging (SRM) algorithm to detect small regions, and then Harris interest points are localized in angular radial partition (ARP) of a circular region which are invariant to rotation and scale transformations. Moreover, feature vectors for a circular patch around Harris points are extracted using Hӧlder estimation regularity based descriptor (HGP-2) to reduce false positives. In the second approach, we therefore proposed a forensic algorithm to recognize the blurred duplicate regions in a synthesized forged image efficiently, especially when the forged region in the images is small. The method is based on blur metric evaluation (BME) and phase congruency (PCy). In the third approach, we proposed a detection method to reveal the forgery under illumination variations. The proposed method used Hessian to detect the keypoints and their corresponding features are represented by robust descriptor known as Center symmetric local binary pattern (CSLBP). The proposed methods be evaluated on two benchmark datasets. The first one is MICC-F220 which contains 220 JPEG images. The second dataset is an image manipulation dataset which includes 48 PNG true color. The experimental results illustrate that the proposed algorithms are robust against several geometric changes, such as JPEG compression, rotation, noise, blurring, illumination variations, and scaling. Furthermore, the proposed methods are resistant to forgery where small up to 8*8 pixels and flat regions are involved, with little visual structures. The average detection rate of our algorithm maintained 96 % true positive rate and 7 % false positive rate which outperform several current detection methods

Anti-spoofing method for fingerprint recognition using patch based deep learning machine

Today's with increasing identity theft, biometric systems based on fingerprints have a growing importance in protection and access restrictions. Malicious users violate them by presenting fabricated attempts. For example, artificial fingerprints constructed by gelatin, Play-Doh and Silicone molds may be misused for access and identity fraud by forgers to clone fingerprints. This process is called spoofing. To detect such forgeries, some existing methods using handcrafted descriptors have been implemented for assuring user presence. Most of them give low accuracy rates in recognition. The proposed method used Discriminative Restricted Boltzmann Machines to recognize fingerprints accurately against fabricated materials used for spoofing. © 2019 Karabuk Universit

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks