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| BILL7190 | @Forensic analysis methods in digital images | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Gül TAHAOĞLU | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | At the end of the course, students will gain the competence to develop methods that can detect traces of forgery in digital images. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Can identify the type of forgeries in images | 1 | 6, | | PO - 2 : | Can develop methods to detect traces of forgery in images | 3 | 1, | | PO - 3 : | She can perform forgery on the image. | 2 | 6, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Within the scope of the course, theoretical and practical explanations of approaches to detecting forgeries in digital images will be provided. Recent studies in the fields of copy-paste forgery, image fusion forgery, and deep fake image detection will be examined and the latest studies in the field will be followed up to date. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Digital Camera Image Formation: Introduction and Hardware. . | | | Week 2 | Digital Camera Image Formation: Processing and Storage . . | | | Week 3 |
DigitalImageFormats. | | | Week 4 |
SearchingandExtractingDigitalImageEvidence | | | Week 5 | ImageandVideoSourceClassIdentification | | | Week 6 |
SensorDefectsinDigitalImageForensic | | | Week 7 | Source Attribution Based on Physical Defects in Light Path | | | Week 8 | | | | Week 9 | MidTerm Exam | | | Week 10 | Natural Image Statistics in Digital Image Forensics | | | Week 11 | Detecting Doctored Images | | | Week 12 | | | | Week 13 | Discrimination of Computer Synthesized or Recaptured Images fromRealImages | | | Week 14 | Courtroom Considerations in Digital Image Forensics | | | Week 15 | Counter-Forensics:AttackingImageForensics | | | Week 16 | Deep Fake Image Generation | | | |
| 1 | Irene Amerini, Gianmarco Baldini, Francesco Leotta, İmage and Video Forensic, January 2022, Journal of Imaging | | | |
| 1 | Digital Image Forensics: There is More to a Picture than Meets the Eye, Hüsrev Taha Sencar, Nasir Memon, 2013, Springer | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | In-term studies (second mid-term exam) | 14 | | 2 | 20 | | End-of-term exam | 14 | | 2 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 1 | 9 | 9 | | Arasınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 1 | 14 | 14 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 97 |
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