Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey by Nam Thanh Pham, Chun-Su Park

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An efficient convolution neural network method for copy-move video forgery detection by Mohamed Meselhy Eltoukhy, Faisal S. Alsubaei, Akram M. Mortda, Khalid M. Hosny

Subjects: “…Unmanned Systems, Copy-Move Forgery…”
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A robust detection and localization technique for copy-move forgery in digital images by Manaf Mohammed Ali Alhaidery, Amir Hossein Taherinia, Haider Ismael Shahadi

Subjects: “…Copy Move Forgery Detection (CMFD)…”
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Detection of image copy-move forgery using local intensity order pattern by Jing LIN, Tian-qiang HUANG, Ling-peng LIN, Xiao-chen1 LI

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Establishment of Goat Milk Forgery Detection Method Based on Casein Rapid Immunoassay by Changrui XING, Chongjing LIU, Zhikang KONG, Xiaoxu ZHAO, Junqin QIAO, Jian YUAN, Hongzhen LIAN

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The Head Pendant from the Marchetti Collection: Phoenicio-Punic Glass or Modern Forgery? by Elisabetta Malaman, Cinzia Bettineschi, Ivana Angelini, Monica Salvadori

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Selected aspects of document forgery in the context of Article 5 of the Constitution of the Republic of Poland by Dorota Semków

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A Comparative Study of Bayesian and Dempster-Shafer Fusion on Image Forgery Detection by Anh-Thu Phan-Ho, Florent Retraint

Subjects: “…Forgery localization…”
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Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive Frames by SADDIQUE, M., ASGHAR, K., BAJWA, U. I., HUSSAIN, M., HABIB, Z.

“…During the process of spatial forgery, the texture and micro-patterns of the frames become inconsistent, which can be observed in the difference of two consecutive frames. …”
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Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework by Prof.Dr. j.k. Sumathi

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Gender forgery of faces by fusing wavelet shortcut connection generative adversarial network by Wanze CHEN, Liqing HUANG, Jiazhen CHEN, Feng YE, Tianqiang HUANG, Haifeng LUO

“…The mainstream methods in the field of facial attribute manipulation had the following two defects due to data and model architecture limitations.First, the bottleneck structure of the autoencoder model results in the loss of feature information, and the traditional method of continuously injected styles to the source domain features during the decoding process makes the generated image too referential to the target domain while losing the identity information and fine-grained details.Second, differences in facial attributes composition between images, such as gender, ethnicity, or age can cause variations in frequency domain information.And the current unsupervised training methods do not automatically adjust the proportion of source and target domain information in the style injection stage, resulting in artifacts in generated images.A facial gender forgery model based on generative adversarial networks and image-to-image translation techniques, namely fused wavelet shortcut connection generative adversarial network (WscGAN), was proposed to address the these issues.Shortcut connections were added to the autoencoder structure, and the outputs of different encoding stages were decomposed at the feature level by wavelet transform.Attention mechanism was employed to process them one by one, to dynamically change the proportion of source domain features at different frequencies in the decoding process.This model could complete forgery of facial images in terms of gender attributes.To verify the effectiveness of the model, it was conducted on the CelebA-HQ dataset and the FFHQ dataset.Compared with the existing optimal models, the method improves the FID and LPIPS indices by 5.4% and 11.2%, and by 1.8% and 6.7%, respectively.Furthermore, the effectiveness of the proposed method in improving the gender attribute conversion of facial images is fully demonstrated by the results based on qualitative visual comparisons.…”
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Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering by Guiwei Fu, Yujin Zhang, Yongqi Wang

“…Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move forgery detection algorithm based on fused features and density clustering. …”
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Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies by Bo Liu, Chi-Man Pun, Xiao-Chen Yuan

“…Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. …”
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Robust face forgery detection integrating local texture and global texture information by Rongrong Gong, Ruiyi He, Dengyong Zhang, Arun Kumar Sangaiah, Mohammed J. F. Alenazi

Subjects: “…Face forgery detection…”
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SMDAF: A novel keypoint based method for copy‐move forgery detection by Guangyu Yue, Qing Duan, Renyang Liu, Wenyu Peng, Yun Liao, Junhui Liu

“…Abstract Copy–move forgery poses a significant threat to social life and has aroused much attention in recent years. …”
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Forgery face detection method based on multi-domain temporal features mining by Chuntao ZHU, Chengxi YIN, Bolin ZHANG, Qilin YIN, Wei LU

“…Financial technology has greatly facilitated people’s daily life with the continuous development of computer technology in the financial services industry.However, digital finance is accompanied by security problems that can be extremely harmful.Face biometrics, as an important part of identity information, is widely used in payment systems, account registration, and many other aspects of the financial industry.The emergence of face forgery technology constantly impacts the digital financial security system, posing a threat to national asset security and social stability.To address the security problems caused by fake faces, a forgery face detection method based on multi-domain temporal features mining was proposed.The tampering features were distinguished and enhanced based on the consistency of statistical feature data distribution and temporal action trend in the temporal features of videos existing in the spatial domain and frequency domain.Temporal information was mined in the spatial domain using an improved LSTM, while in the frequency domain, temporal information existing in different frequency bands of the spectrum was mined using 3D convolution layers.The information was then fused with the tampering features extracted from the backbone network, thus effectively distinguishing forged faces from real ones.The effectiveness of the proposed method was demonstrated on mainstream datasets.…”
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Afropolitan Masculinity: Forgeries of Wife-Owning Husbands in West Africa, 1850s–1950s by Ndubueze L. Mbah

“…One arena of forgery examined in this article entailed the invention of “husband” as “wife-owner,” within a context of gendered aspirations for social reproduction in the age of abolition. …”
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A Meta-Learning Approach for Few-Shot Face Forgery Segmentation and Classification by Yih-Kai Lin, Ting-Yu Yen

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Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study by Krzysztof Zarzycki, Patryk Chaber, Krzysztof Cabaj, Maciej Ławryńczuk, Piotr Marusak, Robert Nebeluk, Sebastian Plamowski, Andrzej Wojtulewicz

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Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour by Kiran, Bharath, K.N., Gururaj Harinahalli Lokesh, Francesco Flammini, D.S. Sunil Kumar

“…The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. …”
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