SAHTE MULTİMEDYA İÇERİK ÜRETİMİ VE TESPİTİNDE GELENEKSEL VE DERİN ÖĞRENME TABANLI YAKLAŞIMLAR

Öz

Derin öğrenme teknolojilerinin, özellikle Çekişmeli Üretici Ağların (Generative Adversarial Networks, GAN) ortaya çıkışı ve gelişimi, Derin Sahte (Deepfake) olarak bilinen yüksek gerçekçi sentetik multimedyanın ortaya çıkmasına yol açmıştır. Bu doğal olmayan, yapay oluşturulan medya dosyaları, dijital içeriğin bütünlüğü için önemli bir tehdit oluşturmakta, toplumsal güveni zedelemekte ve yanlış bilgi yayımı ile siber suçlarda kullanılabilmektedir. Bu çalışmada, sahte multimedya üretim yöntemleri ve bunların tespitine yönelik yaklaşımlar üzerine mevcut literatür sistematik olarak incelenmektedir. İnceleme, gürültü, doku ve geometrik özellik analizine dayanan geleneksel el yapımı (hand-crafted) yöntemlerin yanı sıra, derin öğrenmeye dayalı modern yaklaşımları da kapsamaktadır. Bunların arasında Evrişimsel Sinir Ağları (Convolutional Neural Networks, CNN) ve transformerler yer almaktadır. Özellikle model çıkış verilerinin dağılımındaki anomaliler, piksel ilişkilerindeki tutarsızlıklar, frekans alanındaki artefaktlar ve manipülasyon izleri gibi sentetik içeriklere özgü artefaktların tespiti üzerinde durulmaktadır. Sunulan çalışma, mevcut yaklaşımların özetlenmesini, sınırlılıklarının belirlenmesini ve sahte multimedyanın otomatik tespiti teknolojilerinin geliştirilmesi için gelecekteki yönlerin tespit edilmesini sağlamaktadır.

Anahtar Kelimeler

• Derin Öğrenme
• Derin Sahte
• Yapay Zeka
• Manipülasyon
• Sahte Multimedya Tespiti

Etik Beyan

Çalışmada veri toplama ve kullanımında gerekli yasal izinleri aldığımı, diğer kaynaklardan aldığım bilgileri ana metin ve referanslarda eksiksiz gösterdiğimi, araştırma verilerine ve sonuçlarına ilişkin çarpıtma ve/veya sahtecilik yapmadığımı, çalışmam süresince bilimsel araştırma ve etik ilkelerine uygun davrandığımı beyan ederim.

TRADITIONAL AND DEEP LEARNING-BASED APPROACHES FOR FAKE MULTIMEDIA CONTENT GENERATION AND DETECTION

Öz

The emergence and advancement of deep learning technologies, particularly Generative Adversarial Networks (GANs), have enabled the generation of highly realistic synthetic multimedia content, commonly referred to as deepfakes. Such artificially generated media pose a significant threat to the integrity of digital content, undermine public trust, and may be exploited for misinformation dissemination and cybercriminal activities. This paper presents a systematic review of existing literature on fake multimedia content generation methods and the approaches proposed for their detection. The review encompasses both traditional hand-crafted methods based on noise, texture, and geometric feature analysis, as well as modern deep learning-based approaches, including Convolutional Neural Networks (CNNs) and transformers. Particular emphasis is placed on the detection of artifacts specific to synthetic content, such as anomalies in the distribution of model output data, inconsistencies in pixel relationships, artifacts in the frequency domain, and traces of manipulation. The presented study provides a comprehensive summary of existing approaches, identifies their limitations, and outlines future research directions for the development of automated fake multimedia detection technologies.

Anahtar Kelimeler

• Deep Learning
• Deepfake
• Artificial Intelligence
• Manipulation
• Fake Multimedia Detection

Etik Beyan

I declare that I have obtained the necessary legal permissions for data collection and use in this study, that I have fully cited information obtained from other sources in the main text and references, that I have not distorted and/or falsified research data and results, and that I have acted in accordance with scientific research and ethical principles throughout my work.

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