Face image manipulation detection based on a convolutional neural network

Dang, L. Minh; Hassan, Syed Ibrahim; Im, Sungbin; Moon, Hyeonjoon

doi:10.1016/j.eswa.2019.04.005

Cited by 88 publications

(37 citation statements)

References 23 publications

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“…Two different datasets that are used to verify the performance of the proposed model are manipulated face (MANFA) dataset [13] and progressive growing of GANs (PGGAN) dataset [7]. MANFA dataset is used to check the performance of manipulated face images identification.…”

Section: Datasetmentioning

confidence: 99%

“…MANFA dataset is a face image tampered dataset involves only face regions and dedicates to tampered face identification task [13]. Some of the images taken from MANFA dataset are shown in Figure 2.…”

Section: Manfa Dataset (Dataset 1)mentioning

confidence: 99%

“…Image tampering detection research also suffers from imbalanced dataset problem (IDP) [12], which leads to the poor performance of the models on the minority class. For example, IDP appears in the standard two-class classification when the number of tampered images (minority class) is significantly lower than the number of real images (majority class) [13]. As a consequence, ML algorithms classify the testing samples based on features extracted from the majority class and ignore features extracted from samples of the minority class [12].…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, ML algorithms classify the testing samples based on features extracted from the majority class and ignore features extracted from samples of the minority class [12]. There are four main techniques to cope with the IDP, namely: algorithm-based techniques [14,15], data-based techniques [16,17], cost-sensitive learning techniques [18,19], and ensemble-based techniques [13,20].…”

Section: Introductionmentioning

confidence: 99%

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Tampered and Computer-Generated Face Images Identification Based on Deep Learning

et al. 2020

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Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulation based on applying machine learning algorithms on tampered face datasets in the last few years. This paper introduces a deep learning-based framework that can identify manipulated facial images and GAN-generated images. It is comprised of multiple convolutional layers, which can efficiently extract features using multi-level abstraction from tampered regions. In addition, a data-based approach, cost-sensitive learning-based approach (class weight), and ensemble-based approach (eXtreme Gradient Boosting) is applied to the proposed model to deal with the imbalanced data problem (IDP). The superiority of the proposed model that deals with an IDP is verified using a tampered face dataset and a GAN-generated face dataset under various scenarios. Experimental results proved that the proposed framework outperformed existing expert systems, which has been used for identifying manipulated facial images and GAN-generated images in terms of computational complexity, area under the curve (AUC), and robustness. As a result, the proposed framework inspires the development of research on image forgery identification and enables the potential to integrate these models into practical applications, which require tampered facial image detection.

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Section: Datasetmentioning

confidence: 99%

Section: Manfa Dataset (Dataset 1)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tampered and Computer-Generated Face Images Identification Based on Deep Learning

et al. 2020

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“…In [11], the performance of the fake face image detection was further improved by adopting the most advanced CNN-Xception network [12]. In [13], a manipulated face detection algorithm was proposed based on a hybrid ensemble learning approach. However, none of these studies has investigated the fully generated image, but instead, they have been focused only on partial manipulation of face images; thus, they cannot be used to detect the fully generated fake images.…”

Section: Introductionmentioning

confidence: 99%

Deep Fake Image Detection Based on Pairwise Learning

2020

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Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.

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