Forgery of Icons by Julia Spies

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Forgeries of the Seals of the Kalmyk Rulers by Bembya L. Mitruev

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David A. Scott, Authenticity, Restoration, Forgery by Muriel Verbeeck-Boutin

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Exposing Image Forgery by Detecting Consistency of Shadow by Yongzhen Ke, Fan Qin, Weidong Min, Guiling Zhang

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Forgery attacks on several certificateless signature schemes by Jing WANG, Zumeng LI

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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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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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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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New forgery attack on the authenticated cipher SCREAM and iSCREAM by Yu-dan TIAN, Yong-zhuang WEI

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Seam-Carving forgery detection based on expanded Markov features by Guo-rui SHENG, Tie-gang GAO, Li FAN, Lin GAO, Fu-sheng YANG, Shun ZHANG

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Lip forgery detection via spatial-frequency domain combination by Jiaying LIN, Wenbo ZHOU, Weiming ZHANG, Nenghai YU

Subjects: “…DeepFake forgery…”
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Region Duplication Forgery Detection Technique Based on SURF and HAC by Parul Mishra, Nishchol Mishra, Sanjeev Sharma, Ravindra Patel

“…Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. …”
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A dual benchmarking study of facial forgery and facial forensics by Minh Tam Pham, Thanh Trung Huynh, Thanh Tam Nguyen, Thanh Toan Nguyen, Thanh Thi Nguyen, Jun Jo, Hongzhi Yin, Quoc Viet Hung Nguyen

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Image Forgery Detection Techniques: Latest Trends and Key Challenges by Poulomi Deb, Subhrajyoti Deb, Abhijit Das, Nirmalya Kar

Subjects: “…Active forgery…”
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Scientific investigation of copies, fakes and forgeries (Paul Craddock,ed.) by Nico Broers

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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

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FI-SURF algorithm for image copy-flip-move forgery detection by IYan L, IUNian L, HANGBin Z, UANKai-guo Y, ANGYi-xian Y

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Physical Properties Evaluation of Rice Bran Forgery with Corn Cob Addition by Ridla Muhammad, Rima Martin, Nahrowi Nahrowi, Nahdhiyah Alhasanah, Markistiandi Fadhilah

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Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection by Zishuo Guo, Baopeng Zhang, Jack Fan, Zhu Teng, Jianping Fan

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