AMEET Inventory

Shehnaz, Syed Ilyas; Premadasa, Gamini; Arifulla, Mohamed; Sreedharan, Jayadevan; Gomathi, Kadayam Guruswami

doi:10.1037/t44325-000

Cited by 2 publications

(3 citation statements)

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“…However, this method cannot localize frame duplication and frame shuffling attacks. Many other authors, such as Zhang et al [13], Liao and Huang [51], Zhao et al [52], Bakas et al [53], Kharat et al [15], and Shehnaz and Kaur [54], utilized texture features to detect inter-frame tampering in a video. These techniques yield good results; however, these methods are computationally extensive due to their high dimensional features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Deep learning techniques rely heavily on large datasets to automatically extract the high-dimensional features essential for video tampering detection. Numerous researchers have carried out experiments on their developed datasets [17,21,25,29,40,43,52,54,57], yielding commendable detection accuracies; however, these datasets are not accessible to other researchers. A thorough analysis reveals that most of the existing inter-frame tampering detection methods are based on handcrafted features, which are sensitive to post-processing operations like blurring, noise, and compression.…”

Section: Literature Reviewmentioning

confidence: 99%

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DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

Akhtar,

Hussain,

Habib

2024

Mathematics

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Inter-frame tampering in surveillance videos undermines the integrity of video evidence, potentially influencing law enforcement investigations and court decisions. This type of tampering is the most common tampering method, often imperceptible to the human eye. Until now, various algorithms have been proposed to identify such tampering, based on handcrafted features. Automatic detection, localization, and determine the tampering type, while maintaining accuracy and processing speed, is still a challenge. We propose a novel method for detecting inter-frame tampering by exploiting a 2D convolution neural network (2D-CNN) of spatiotemporal information and fusion for deep automatic feature extraction, employing an autoencoder to significantly reduce the computational overhead by reducing the dimensionality of the feature’s space; analyzing long-range dependencies within video frames using long short-term memory (LSTM) and gated recurrent units (GRU), which helps to detect tampering traces; and finally, adding a fully connected layer (FC), with softmax activation for classification. The structural similarity index measure (SSIM) is utilized to localize tampering. We perform extensive experiments on datasets, comprised of challenging videos with different complexity levels. The results demonstrate that the proposed method can identify and pinpoint tampering regions with more than 90% accuracy, irrespective of video frame rates, video formats, number of tampering frames, and the compression quality factor.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

Akhtar,

Hussain,

Habib

2024

Mathematics

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“…The removal efficiency (%) was determined using Equation ( 3), and the distribution coefficient (Kd) of the H-TTAPA resin was calculated using Equation (4) [44][45][46][47][48]:…”

Section: Batch Adsorption Experimentsmentioning

confidence: 99%

Sequestration of Toxic Metal Ions from Industrial Effluent Using the Novel Chelating Resin Tamarind Triazine Amino Propanoic Acid (TTAPA)

Mandal

Abomuti

Al‐Harbi

et al. 2023

Water

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Due to higher levels of industrial activity, the concentrations of toxic substances in natural water bodies are increasing. One of the most dangerous groups of toxic compounds is heavy metals, with even trace amounts of most heavy metals being harmful to aquatic life. This is why purifying water has become an urgent priority. In this context, ion-exchange resins have become more widely used in water treatment processes. However, to reduce the costs and improve the sustainability of this strategy, natural resins are favored over synthetic versions. Therefore, in the present study, a natural tamarind-based chelating resin was developed. The tamarind triazine amino propanoic acid (TTAPA) resin was synthesized and characterized using Fourier-transform infrared spectroscopy, thermogravimetry analysis, scanning electron microscopy, elemental analysis, and physicochemical analysis of the moisture content, total ion-exchange capacity, bulk volume, bulk density, and percentage nitrogen content. The biological oxygen demand and chemical oxygen demand of the industrial effluent before and after treatment were also analyzed. The batch analysis was used to determine the distribution coefficient and percentage removal of the metal ions Fe(II), Zn(II), Pb(II), Cu(II), and Cd(II). The removal efficiency of the prepared TTAPA resin was highest for Fe(II), followed by Cu(II), Zn(II), Pb(II), and Cd(II) in order. The chelating ion-exchange resin also had a metal ion recovery of more than 95%, thus demonstrating great promise for the sequestration of heavy metal ions from industrial wastewater. The proposed TTAPA resin is biodegradable, non-toxic, cost-effective, reproducible, and eco-friendly.

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AMEET Inventory

Cited by 2 publications

References 0 publications

DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

DEEP-STA: Deep Learning-Based Detection and Localization of Various Types of Inter-Frame Video Tampering Using Spatiotemporal Analysis

Sequestration of Toxic Metal Ions from Industrial Effluent Using the Novel Chelating Resin Tamarind Triazine Amino Propanoic Acid (TTAPA)

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