Passive approaches for digital image forgery detection by Pravin Kakar

“…We have adapted these tools to work with image forgeries, where the set of problems faced is different from CBIR. …”
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Novel forgery mechanisms in multivariate signature schemes by Abdul Jamal, Nurul Amiera Sakinah, Kamel Ariffin, Muhammad Rezal, Abdullah, Kamilah

“…In this paper, we present three multivariate digital signature forgery mechanisms by a rogue service provider. We also lay out techniques to identify two of such mechanisms. …”

Copy-move forgery detection in digital image by Alamro, Loai, Yusoff, Nooraini

“…Copy-move is considered as one of the most popular kind of digital image tempering, in which one or more parts of a digital image are copied and pasted into different locations.Geometric transformation is among the major challenges in detecting copy-move forgery of a digital image.In such forgery, the copied and moved parts of a forged image are either rotated or/and re-scaled.Hence, in this study we propose a combination of Discrete Wavelet Transform (DWT) and Speeded Up Robust Features (SURF) to detect a copy-move activity.The experiments results prove that the proposed method is superior with overall accuracy 95%. …”

Email header forgery detection mechanism [electronic resource] / by Adnan Abdirahman Adami, 1984-

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Ensemble Approach for Image Recompression-Based Forgery Detection by Se-Jun Ham, Van-Ha Hoang, Chun-Su Park

Subjects: “…Image forgery detection…”
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An Optimal Hybrid Method to Detect Copy-move Forgery by Fatemeh Zare mehrjardi, Alimohammad Latif, Mohsen Sardari Zarchi

Subjects: “…copy-move forgery…”
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Countering criminal offenses of forgery of documents in the Republic of Serbia by Jovašević Dragan

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Deep forgery discriminator via image degradation analysis by Miaomiao Yu, Jun Zhang, Shuohao Li, Jun Lei, Fenglei Wang, Hao Zhou

“…Most of the existing detection methods are only suitable for one type of forgery and only work for low‐quality tampered images, restricting their applications. …”
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Detection of tamper forgery image in security digital mage by Mohammed Fakhrulddin Abdulqader, Adnan Yousif Dawod, Ann Zeki Ablahd

“…The most common types of picture forgery are copy-move forgery and splicing images. …”
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Blur detection of digital forgery using mathematical morphology by Hui-fen HUANG,Zhi-hong WANG,Yu-hong CHANG

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Survey on adversarial attacks and defense of face forgery and detection by Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO

“…Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and causing significant social harm.As a result, it is crucial to develop effective detection methods to identify fake videos.In recent years, deep learning technology has made the task of face forgery and detection more accessible.Deep learning-based face forgery methods can generate highly realistic faces, while deep learning-based fake face detection methods demonstrate higher accuracy compared to traditional approaches.However, it has been shown that deep learning models are vulnerable to adversarial examples, which can lead to a degradation in performance.Consequently, games involving adversarial examples have emerged in the field of face forgery and detection, adding complexity to the original task.Both fakers and detectors now need to consider the adversarial security aspect of their methods.The combination of deep learning methods and adversarial examples is thus the future trend in this research field, particularly with a focus on adversarial attack and defense in face forgery and detection.The concept of face forgery and detection and the current mainstream methods were introduced.Classic adversarial attack and defense methods were reviewed.The application of adversarial attack and defense methods in face forgery and detection was described, and the current research trends were analyzed.Moreover, the challenges of adversarial attack and defense for face forgery and detection were summarized, and future development directions were discussed.…”
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An Inter-Frame Forgery Detection Algorithm for Surveillance Video by Qian Li, Rangding Wang, Dawen Xu

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Keypoint based comprehensive copy‐move forgery detection by Anjali Diwan, Rajat Sharma, Anil K. Roy, Suman K. Mitra

“…The simplest of the image tampering tricks is the copy‐move forgery. In copy‐move forgery copied portion of the image is pasted on another part of the same image. …”
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Blur detection of digital forgery using mathematical morphology by Hui-fen HUANG, Zhi-hong WANG, Yu-hong CHANG

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Noise-attention-based forgery face detection method by Bolin ZHANG, Chuntao ZHU, Qilin YIN, Jingqiao FU, Lingyi LIU, Jiarui LIU, Hongmei LIU, Wei LU

“…With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method.…”
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Forgery Detection of Documents: A Review in Digital Forensics by Basim Mahmood, Khalid Almukhtar, Alaa Amged

Subjects: “…digital forensic؛ ؛forgery؛ ؛imitation؛ ؛؛…”
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Survey on adversarial attacks and defense of face forgery and detection by Shiyu HUANG, Feng YE, Tianqiang HUANG, Wei LI, Liqing HUANG, Haifeng LUO

“…Face forgery and detection has become a research hotspot.Face forgery methods can produce fake face images and videos.Some malicious videos, often targeting celebrities, are widely circulated on social networks, damaging the reputation of victims and causing significant social harm.As a result, it is crucial to develop effective detection methods to identify fake videos.In recent years, deep learning technology has made the task of face forgery and detection more accessible.Deep learning-based face forgery methods can generate highly realistic faces, while deep learning-based fake face detection methods demonstrate higher accuracy compared to traditional approaches.However, it has been shown that deep learning models are vulnerable to adversarial examples, which can lead to a degradation in performance.Consequently, games involving adversarial examples have emerged in the field of face forgery and detection, adding complexity to the original task.Both fakers and detectors now need to consider the adversarial security aspect of their methods.The combination of deep learning methods and adversarial examples is thus the future trend in this research field, particularly with a focus on adversarial attack and defense in face forgery and detection.The concept of face forgery and detection and the current mainstream methods were introduced.Classic adversarial attack and defense methods were reviewed.The application of adversarial attack and defense methods in face forgery and detection was described, and the current research trends were analyzed.Moreover, the challenges of adversarial attack and defense for face forgery and detection were summarized, and future development directions were discussed.…”
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Generalization of Forgery Detection With Meta Deepfake Detection Model by Van-Nhan Tran, Seong-Geun Kwon, Suk-Hwan Lee, Hoanh-Su Le, Ki-Ryong Kwon

“…Face forgery generating algorithms that produce a range of manipulated videos/images have developed quickly. …”
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Noise-attention-based forgery face detection method by Bolin ZHANG, Chuntao ZHU, Qilin YIN, Jingqiao FU, Lingyi LIU, Jiarui LIU, Hongmei LIU, Wei LU

“…With the advancement of artificial intelligence and deep neural networks, the ease of image generation and editing has increased significantly.Consequently, the occurrence of malicious tampering and forgery using image generation tools is on the rise, posing a significant threat to multimedia security and social stability.Therefore, it is crucial to research detection methods for forged faces.Face tampering and forgery can occur through various means and tools, leaving different levels of forgery traces during the tampering process.These traces can be partly reflected in the image noise.From the perspective of image noise, the noise components reflecting tampering traces of forged faces were extracted through a noise removal module.Furthermore, noise attention was generated to guide the backbone network in the detection of forged faces.The training of the noise removal module was supervised using SRM filters.In order to strengthen the guidance of the noise removal module, the noise obtained by the noise removal module was added back to the real face image, forming a pair of supervised training samples in a self-supervised manner.The experimental results illustrate that the noise features obtained by the noise removal module have a good degree of discrimination.Experiments were also conducted on several public datasets, and the proposed method achieves an accuracy of 98.32% on the Celeb-DF dataset, 92.61% on the DFDC dataset, and more than 94% on the FaceForensics++ dataset, thus proving the effectiveness of the proposed method.…”
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Statistical image source model identification and forgery detection by Cao, Hong

“…These forensics tools help expose common image forgeries, especially those easy-to-make forgeries, which can hardly be seen directly by human eyes. …”
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