Detecting Facial Retouching Using Supervised Deep Learning

Bharati, Aparna; Singh, Richa; Vatsa, Mayank; Bowyer, Kevin W.

doi:10.1109/tifs.2016.2561898

Cited by 115 publications

(93 citation statements)

References 13 publications

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“…In [75], the authors propose Gaussian-Neuron CNN (GNCNN) for steganalysis. A deep learning approach to identify facial retouching was proposed in [11]. In [88], image region forgery detection has been performed using stacked auto-encoder model.…”

Section: Related Workmentioning

confidence: 99%

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Bappy

Simons

Nataraj

et al. 2019

IEEE Trans. on Image Process.

368

207

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With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets.

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Section: Related Workmentioning

confidence: 99%

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Bappy

Simons

Nataraj

et al. 2019

IEEE Trans. on Image Process.

368

207

View full text Add to dashboard Cite

show abstract

“…Retouching, makeup detection, face spoofing and morphing are widely studied areas, that can be considered similar to retouching detection. Recent work by Bharati et al [5] makes use of supervised deep Boltzmann machine algorithm for detecting retouching on the ND-IIITD database. It also introduces the ND-IIITD dataset which consists of 2600 original and 2275 retouched images.…”

Section: Related Workmentioning

confidence: 99%

“…The input to the SVM is the output obtained from equation (4). As retouching has been introduced in non-facial regions of the image in the ND-IIITD database [5], all the patches of the retouched images are considered as tampered. For GANs based evaluation, specific regions that are classified using GANs are labeled as tampered.…”

Section: Image Classificationmentioning

confidence: 99%

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On Detecting GANs and Retouching based Synthetic Alterations

Jain

Singh

Vatsa

2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

Self Cite

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Digitally retouching images has become a popular trend, with people posting altered images on social media and even magazines posting flawless facial images of celebrities. Further, with advancements in Generative Adversarial Networks (GANs), now changing attributes and retouching have become very easy. Such synthetic alterations have adverse effect on face recognition algorithms. While researchers have proposed to detect image tampering, detecting GANs generated images has still not been explored. This paper proposes a supervised deep learning algorithm using Convolutional Neural Networks (CNNs) to detect synthetically altered images. The algorithm yields an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset. It outperforms the previous state of the art which reported an accuracy of 87% on the database. For distinguishing between real images and images generated using GANs, the proposed algorithm yields an accuracy of 99.83%.

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“…Recently, deep neural network-based approaches have become an efficient tool for anti-spoofing systems. 11,21,22,[40][41][42][43][44] In particular, deep convolutional neural network (D-CNN) architecture is the most widely used discriminative feature learning from 2D images. Since the results are so encouraging, it becomes a new trend in face anti-spoofing research community.…”

Section: Related Workmentioning

confidence: 99%

Cover patches: A general feature extraction strategy for spoofing detection

Cai

Leng

et al. 2018

Concurrency and Computation

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Summary Face anti‐spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti‐spoofing technique is still a challenging task. Mainstream image‐based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi‐patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand‐crafted Gray Level Co‐occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti‐spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti‐spoofing databases.

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Detecting Facial Retouching Using Supervised Deep Learning

Cited by 115 publications

References 13 publications

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries

On Detecting GANs and Retouching based Synthetic Alterations

Cover patches: A general feature extraction strategy for spoofing detection

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