The rapid advancement of synthetic media, while beneficial, has also spawned GAN-generated deepfakes, which pose risks, including misinformation and digital fraud. This paper investigates the detectability of GAN-generated static images, focusing on residual artifacts that are imperceptible to humans but detectable through digital analysis. Our approach introduces three key advancements: (1) a taxonomy for classifying GAN residues in deepfake detection; (2) a unique mixed dataset combining StyleGAN3, ProGAN, and InterfaceGAN to aid cross-model detection research; and (3) a combination of frequency space analysis and RGB color correlation methods to improve artifact detection. Covering three different transform methods, three GAN models, and twelve classification methods, ours is the most comprehensive study of detection of static deepfake face images produced by GANs. Our results demonstrate that artifact-based detection can achieve high accuracy, precision, recall, and F1 scores, challenging prior assumptions about the detectability of synthetic face images.

Deepfake videolar özellikle son yıllarda çok fazla dikkat çeken alanlardan birisidir. Sosyal ağların artan popülaritesi neticesinde mobil cihazların gelişmiş kameraları ile oluşturulan video ve görüntülerin düzenlenmesi ve paylaşılması geçmiş döneme göre daha erişilebilir bir düzeye ulaşmıştır. Deepfake tekniği ile oluşturulan ve sosyal ağlarda dağıtımı yapılan birçok sahte görüntü ve video sadece kişilerin özel hayatını değil, aynı zamanda toplum düzenini de tehdit etmektedir. İnsan yüzü, insanlar arası etkileşimde ve biyometrik tabanlı doğrulama sistemlerinde halihazırda önemli bir role sahiptir. Bu nedenle, yüz karelerinde küçük çaplı manipülasyonlar dahi, güvenlik uygulamalarına ve sayısal verilere olan güveni sarsabilecektir. Bu çalışmada, deepfake video tespiti modelinin oluşturulmasında bir sınıflandırma probleminin çözümü yaklaşımı benimsenmiştir. Öznitelik çıkarıcı olarak önceden eğitilmiş EfficientNet model ailesi kullanılmış ve tahminin çıktısını elde etmek için bunun üzerinde bir sınıflandırıcı eğitilmiştir. Modelin eğitilmesinde yine derin öğrenme tabanlı yöntemlerle üretilen ve en büyük deepfake veri setlerinden olan DFDC veri seti kullanılmıştır. Derin öğrenme algoritma ve kütüphanelerinden yararlanılmış ve belirlenen videonun gerçek mi yoksa sahte mi olduğuna karar veren yeni bir model ortaya koyulmuştur.

Given the rapid advancement of deepfake technology, which allows for the creation of highly realistic fake content, there is a pressing need for an efficient solution to address the security risks associated with this technology. Deepfake videos are widely recognized for their significant implications, including the potential for identity theft, the dissemination of false information, and the endangerment of national security. Therefore, it is crucial to develop and enhance the reliability of deepfake detection algorithms. In this study, feature extraction techniques were performed to utilize deep learning algorithms such as Xception and ResNet50 to detect deepfakes in a video dataset using the DFDC dataset. Additionally, a total of eight hybrid models were developed using various classification algorithms such as SVM, KNN, MLP, and RF. The ResNet50 and RF hybrid models achieved the highest accuracy rate of 98%, with an AUC value of 99.65%. This study presents a machine learning method that has been developed to address different technical challenges in the field of deepfake detection and effectively identify deepfakes. The proposed method has demonstrated successful performance compared to state-of-the-art models, proving its effectiveness in accurately detecting fake content within videos.

In this article, we consider the module theoretic version of I-semiperfect rings R for an ideal I which are defined by Yousif and Zhou ( 2002 Yousif , M. F. , Zhou , Y. ( 2002 ). Semiregular, semiperfect, and perfect rings relative to an ideal . Rocky Mountain J. Math. 32 ( 4 ): 1651 – 1671 . [CSA] [Crossref], [Web of Science ®] [Google Scholar] ). Let M be a left module over a ring R, N ∊ σ[M], and τ _M a preradical on σ[M]. We call N τ _M -semiperfect in σ[M] if for any submodule K of N, there exists a decomposition K = A ⊕ B such that A is a projective summand of N in σ[M] and B ≤ τ _M (N). We investigate conditions equivalent to being a τ _M -semiperfect module, focusing on certain preradicals such as Z _M , Soc, and δ _M . Results are applied to characterize Noetherian QF-modules (with Rad(M) ≤ Soc(M)) and semisimple modules. Among others, we prove that if every R-module M is Soc-semiperfect, then R is a Harada and a co-Harada ring.

Deepfake is the general expression of fake images or videos. Images and videos created with Deep Neural Networks (DNN) can cause both personal and national problems. Deepfake technology has been making progress day by day. Accordingly, both the detection methods and the diversity of the data sets to be trained are increasing. In this article, datasets created in deepfake technology and applications that use datasets for deepfake detection are evaluated. The data sets used are produced with different models. There exist both traditional and deep learning-based detection methods for deepfake detection. Due to the recent malicious use of deepfake technology, detection methods and methods to be used in applications are of great importance. When the detection methods are compared, the accuracy of the deep learning-based detection method is higher for each data set. The method that provides the highest accuracy among two different data sets is the Xception method.