Search

In contexts requiring user authentication, such as financial, legal, and administrative systems, signature verification emerges as a pivotal biometric method. Specifically, handwritten signature verification stands out prominently for document authentication. Despite the effectiveness of triplet loss similarity networks in extracting and comparing signatures with forged samples, conventional deep learning models often inadequately capture individual writing styles, resulting in suboptimal performance. Addressing this limitation, our study employs a triplet loss Siamese similarity network for offline signature verification, irrespective of the author. Through experimentation on five publicly available signature datasets—4NSigComp2012, SigComp2011, 4NSigComp2010, and BHsig260—various distance measure techniques alongside the triplet Siamese Similarity Network (tSSN) were evaluated. Our findings underscore the superiority of the tSSN approach, particularly when coupled with the Manhattan distance measure, in achieving enhanced verification accuracy, thereby demonstrating its efficacy in scenarios characterized by close signature similarity. [ABSTRACT FROM AUTHOR]

The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and sample datasets, where we compare the performance of our proposed model with various state-of-the-art pretrained models such as VGG-19, Xception and Inception-ResNet-v2. Furthermore, we perform the experiments with various resolutions maintaining 1:1 and 9:16 aspect ratios, which have not been explored for DFDC datasets by any other groups to date. Thus, the proposed model can flexibly accommodate various resolutions and aspect ratios, without being constrained to a specific resolution or aspect ratio for any type of image classification problem. While most of the reported research is limited to sample or preview DFDC datasets only, we have also attempted the testing on full DFDC datasets and presented the results. Contemplating the fact that the detailed results and resource analysis for various scenarios are provided in this research, the proposed deepfake detection method is anticipated to pave new avenues for deepfake detection research, that engages with DFDC datasets. [ABSTRACT FROM AUTHOR]

The problem of forged images has become a global phenomenon that is spreading mainly through social media. New technologies have provided both the means and the support for this phenomenon, but they are also enabling a targeted response to overcome it. Deep convolution learning algorithms are one such solution. These have been shown to be highly effective in dealing with image forgery derived from generative adversarial networks (GANs). In this type of algorithm, the image is altered such that it appears identical to the original image and is nearly undetectable to the unaided human eye as a forgery. The present paper investigates copy-move forgery detection using a fusion processing model comprising a deep convolutional model and an adversarial model. Four datasets are used. Our results indicate a significantly high detection accuracy performance (~95%) exhibited by the deep learning CNN and discriminator forgery detectors. Consequently, an end-to-end trainable deep neural network approach to forgery detection appears to be the optimal strategy. The network is developed based on two-branch architecture and a fusion module. The two branches are used to localize and identify copy-move forgery regions through CNN and GAN. [ABSTRACT FROM AUTHOR]