Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection

Bayar, Belhassen; Stamm, Matthew C.

doi:10.1109/tifs.2018.2825953

Cited by 408 publications

(454 citation statements)

References 37 publications

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“…BN, on the other hand, requires coarse level information about image sizes to separately process images of different size for better performance. We show that by leveraging on pre-trained models, the finetuning of the proposed networks not only converges much faster than recent works such as [4,7] but also generalizes well for other tasks like photorealism detection of heterogeneous origin [10].…”

Section: Introductionmentioning

confidence: 72%

“…Since the accuracy is contingent on the sharpness of peak, it is obsevred the performance of the method quickly degrades for patches less than 512 × 512. [3] 1024 × 1024 512 × 512 MISLnet [7] 256 × 256 256 × 256 Li et al [14] 512 × 512 512 × 512 Verma et al [9] 512 × 384 128 × 128 Kirchner et al [15] 1024 × 1024 512 × 512 Bianchi et al [2] 1024 × 1024 512 × 512 Quan et al [10] <1024 × 1024 233 × 233 kernel size proportional to tensor's height and width to reduce all tensors to the same size. However, in our experiments this resulted in poor performance, see results for the Max-Pooling Network (MPN) in Table 3.…”

Section: Iterative Poolingmentioning

confidence: 99%

“…Inspired by the immense success of works like Faster RCNN [11] and YOLO [16] which decided to build on top of existing CNN models, we also chose to use ResNet-18 [17] as our base architecture. Although proposed much earlier than recent CNN models for image forensic [4,7,10] Fig. 2.…”

Section: Iterative Poolingmentioning

confidence: 99%

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Multi-Patch Aggregation Models for Resampling Detection

Lamba

Mitra

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Images captured nowadays are of varying dimensions with smartphones and DSLR's allowing users to choose from a list of available image resolutions. It is therefore imperative for forensic algorithms such as resampling detection to scale well for images of varying dimensions. However, in our experiments we observed that many state-of-the-art forensic algorithms are sensitive to image size and their performance quickly degenerates when operated on images of diverse dimensions despite re-training them using multiple image sizes. To handle this issue, we propose two novel deep neural networks -Iterative Pooling Network (IPN), which does not assume any prior information about the original image size, and Branched Network (BN), which uses this prior knowledge to produce better results. IPN adopts a novel iterative pooling strategy that converts tensors of multiple sizes to tensors of a fixed size, as required by deep learning models with fully connected layers. BN alternatively adopts a branched architecture with dedicated pathways for images of different sizes. The effectiveness of the proposed solution is demonstrated on two problems, resampling detection and photorealism detection, which are generally solved as independent problems with different deep learning models. The code is available at https://github.com/MohitLamba94/ Iterative-Pooling.

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Section: Introductionmentioning

confidence: 72%

Section: Iterative Poolingmentioning

confidence: 99%

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Multi-Patch Aggregation Models for Resampling Detection

Lamba

Mitra

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Finally, five databases are built which are GC, MeanF, GF, MedF and WF. Table 3 depicts the detection accuracy for the proposed method and Bayar's method [32,33] when one manipulation is applied for resampled images. From Table 3, the performances of our and Bayar's methods have different extent of reduction against different post-processing operations.…”

Section: Resampling Detection With Post-processingmentioning

confidence: 99%

Resampling detection of recompressed images via dual-stream convolutional neural network

Cao

Zhou

Huang

et al. 2019

Mathematical Biosciences and Engineering

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Resampling detection plays an important role in identifying image tampering, such as image splicing. Currently, the resampling detection is still difficult in recompressed images, which are yielded by applying resampling followed by post-JPEG compression to primary JPEG images. Except for the scenario of low quality primary compression, it remains rather challenging due to the widespread use of middle/high quality compression in imaging devices. In this paper, we propose a new convolution neural network (CNN) method to learn the resampling trace features directly from the recompressed images. To this end, a noise extraction layer based on low-order high pass filters is deployed to yield the image residual domain, which is more beneficial to extract manipulation trace features. A dual-stream CNN is presented to capture the resampling trails along different directions, where the horizontal and vertical streams are interleaved and concatenated. Lastly, the learned features are fed into Sigmoid/Softmax layer, which acts as a binary/multiple classifier for achieving the blind detection and parameter estimation of resampling, respectively. Extensive experimental results demonstrate that our proposed method could detect resampling effectively in recompressed images and outperform the state-of-the-art detectors.

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“…A blind deep learning method based on CNN [12] was used to learn invisible discrimination artifacts from manipulated images. A constrained convolution layer [13] was proposed to adaptively learn manipulation detection features. CNN-LSTM architecture [14] was developed to detect image forgery by learning the edges of tampered and non-tampered areas.…”

Section: Introductionmentioning

confidence: 99%

Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

Wei

Dong

et al. 2019

Symmetry

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Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.

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Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection

Cited by 408 publications

References 37 publications

Multi-Patch Aggregation Models for Resampling Detection

Multi-Patch Aggregation Models for Resampling Detection

Resampling detection of recompressed images via dual-stream convolutional neural network

Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

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