Detection and Localization of Multiple Image Splicing Using MobileNet V1

Kadam, Kalyani; Ahirrao, Swati; Kotecha, Ketan; Sahu, Sayan

doi:10.1109/access.2021.3130342

Cited by 44 publications

(31 citation statements)

References 37 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This work presents a lightweight model, Mask R-CNN with MobileNet V1, for detecting and identifying copy move and image splicing [ 72 ] forgeries. We have used standard datasets such as COVERAGE, CASIA 2.0, MICC F220, MICC F600, MICC F2000, COLUMBIA, and CASIA 1.0 to evaluate the proposed model for copy move and image splicing forgeries.…”

Section: Discussionmentioning

confidence: 99%

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Kadam

Ahirrao

Kotecha

2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

With the technological advancements of the modern era, the easy availability of image editing tools has dramatically minimized the costs, expense, and expertise needed to exploit and perpetuate persuasive visual tampering. With the aid of reputable online platforms such as Facebook, Twitter, and Instagram, manipulated images are distributed worldwide. Users of online platforms may be unaware of the existence and spread of forged images. Such images have a significant impact on society and have the potential to mislead decision-making processes in areas like health care, sports, crime investigation, and so on. In addition, altered images can be used to propagate misleading information which interferes with democratic processes (e.g., elections and government legislation) and crisis situations (e.g., pandemics and natural disasters). Therefore, there is a pressing need for effective methods for the detection and identification of forgeries. Various techniques are currently employed for the identification and detection of these forgeries. Traditional techniques depend on handcrafted or shallow-learning features. In traditional techniques, selecting features from images can be a challenging task, as the researcher has to decide which features are important and which are not. Also, if the number of features to be extracted is quite large, feature extraction using these techniques can become time-consuming and tedious. Deep learning networks have recently shown remarkable performance in extracting complicated statistical characteristics from large input size data, and these techniques efficiently learn underlying hierarchical representations. However, the deep learning networks for handling these forgeries are expensive in terms of the high number of parameters, storage, and computational cost. This research work presents Mask R-CNN with MobileNet, a lightweight model, to detect and identify copy move and image splicing forgeries. We have performed a comparative analysis of the proposed work with ResNet-101 on seven different standard datasets. Our lightweight model outperforms on COVERAGE and MICCF2000 datasets for copy move and on COLUMBIA dataset for image splicing. This research work also provides a forged percentage score for a region in an image.

show abstract

Section: Discussionmentioning

confidence: 99%

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Kadam

Ahirrao

Kotecha

2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…To further demonstrate the efficiency of the CNN of proposed method, the RMSREs of inertia ratio ρI are compared with AlexNet [39], ResNet [40], and MobileNet [41]. As shown in Fig.…”

Section: Compared With Other Methodsmentioning

confidence: 99%

Cone-Shaped Space Target Inertia Characteristics Identification by Deep Learning with Compressed Dataset

Li¹

2022

Preprint

View full text Add to dashboard Cite

An effective method for identifying inertia characteristics of cone-shaped space target based on deep learning is proposed. The inertia ratio is determined by the time-varying scattering fields from the cone-shaped targets. The multistatic method is introduced to reduce the evaluation time of time-varying scattering fields. The micro-Doppler spectrogram (MDS) dataset is constructed by time-frequency analysis with numerical simulation method, point scattering model, and experimental tests. The compressed dataset is further achieved by truncated singular value decomposition (SVD). Finally, the micro-motion parameter identification model is constructed to identify the inertia ratio for the cone-shaped space target. The interaction loss function and the feed-forward denoising convolutional neural networks (DnCNNs) are employed to improve the identification accuracy. Parameters identification of the precession frequency, precession angle, spin frequency, and inertia ratio with both simulation and experiment datasets demonstrate the validity of the proposed method.

show abstract

“…The lightweight CNN models [4][5][6][7][8] are proposed by using the depthwise separable convolution to reduce the number of model parameters. As one kind of factorized convolutions, the depthwise separable convolution splits the standard convolution into two steps, the depthwise convolution and the pointwise convolution.…”

Section: Lightweight Cnn Modelmentioning

confidence: 99%

“…Thus, with the increasing application demand of CNNs, how to simplify the CNN model and efficiently deploy it onto embedded devices has become a new research hotspot. Using lightweight CNN models, such as Xception [4], Mo-bileNet [5][6][7] and ShuffleNet [8], is an effective way to significantly reduce the number of parameters with limited loss in accuracy. Additionally, using the low-bit data quantization method [9][10][11] can quantify the 32-bit data to 8-bit or even lower, which greatly reduces the size of CNN models.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of an efficient CNN accelerator for low-cost FPGAs

Wang

et al. 2022

IEICE Electron. Express

View full text Add to dashboard Cite

This paper proposes a computation-array-centered dataflow, which adjusts the convolution with different kernel sizes to a unified computing manner and reduces the dimension of computation array from 2D to 1D, so as to maximize the utilization of the computation elements offered by the accelerator. Furthermore, a single unit multiple data (SUMD) strategy is proposed to effectively alleviate the mismatch between the quantized data and the hardware resources with fixed bit width on FPGA. As a case study, an 8-bit MobileNetV2 model has been implemented on the low-cost ZYNQ XC7Z020 FPGA, whose FPS/DSP and GOPS/DSP achieve upto 0.55 and 0.35 respectively.

show abstract

Detection and Localization of Multiple Image Splicing Using MobileNet V1

Cited by 44 publications

References 37 publications

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Efficient Approach towards Detection and Identification of Copy Move and Image Splicing Forgeries Using Mask R-CNN with MobileNet V1

Cone-Shaped Space Target Inertia Characteristics Identification by Deep Learning with Compressed Dataset

Design and implementation of an efficient CNN accelerator for low-cost FPGAs

Contact Info

Product

Resources

About