Yazar "Dirik, Ahmet Emir" seçeneğine göre listele

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    Analysis of Rolling Shutter Effect on ENF-Based Video Forensics
    (Ieee-Inst Electrical Electronics Engineers Inc, 2019) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
    Electric network frequency (ENF) is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) clue to changes in the supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates. These fluctuations in luminance can he captured by video recordings. Accordingly, the ENF can he estimated from such videos by the analysis of steady content in the video scene. When videos are captured by using a rolling shutter sampling mechanism, as is done mostly with CMOS cameras, there is an idle period between successive frames. Consequently, a number of illumination samples of the scene are effectively lost due to the idle period. These missing samples affect the ENF estimation, in the sense of the frequency shift caused and the power attenuation that results. This paper develops an analytical model for videos captured using a rolling shutter mechanism. This model illustrates how the frequency of the main ENF harmonic varies depending on the idle period length, and how the power of the captured ENF attenuates as idle period increases. Based on this, a novel idle period estimation method for potential use in camera forensics that is able to operate independently of video frame rate is proposed. Finally, a novel time-of-recording verification approach based on the use of multiple ENF components, idle period assumptions, and the interpolation of missing ENF samples is also proposed.
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    Detecting the Presence of ENF Signal in Digital Videos: A Superpixel-Based Approach
    (Ieee-Inst Electrical Electronics Engineers Inc, 2017) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
    Electrical network frequency (ENF) instantaneously fluctuates around its nominal value (50/60 Hz) due to a continuous disparity between generated power and consumed power. Consequently, luminous intensity of a mains-powered light source varies depending on ENF fluctuations in the grid network. Variations in the luminance over time can be captured from video recordings and ENF can be estimated through content analysis of these recordings. In ENF-based video forensics, it is critical to check whether a given video file is appropriate for this type of analysis. That is, if ENF signal is not present in a given video, it would be useless to apply ENF-based forensic analysis. In this letter, an ENF signal presence detection method is introduced for videos. The proposed method is based on multiple ENF signal estimations from steady super pixels, i.e., pixels that are most likely uniform in color, brightness, and texture, and intra-class similarity of the estimated signals. Subsequently, consistency among these estimates is then used to determine the presence or absence of an ENF signal in a given video. The proposed technique can operate on video clips as short as 2 min and is independent of the camera sensor type, i.e., CCD or CMOS.
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    FACTORS AFFECTING ENF BASED TIME-OF-RECORDING ESTIMATION FOR VIDEO
    (Ieee, 2019) Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
    ENF (Electric Network Frequency) oscillates around a nominal value (50/60 Hz) due to imbalance between consumed and generated power. The intensity of a light source powered by mains electricity varies depending on the ENF fluctuations. These fluctuations can be extracted from videos recorded in the presence of mains-powered source illumination. This work investigates how the quality of the ENF signal estimated from video is affected by different light source illumination, compression ratios, and by social media encoding. Also explored is the effect of the length of the ENF ground-truth database on time of recording detection and verification.
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    FAULT DIAGNOSIS WITH DEEP LEARNING FOR STANDARD AND ASYMMETRIC INVOLUTE SPUR GEARS
    (American Society of Mechanical Engineers (ASME), 2021) Karpat, Fatih; Dirik, Ahmet Emir; Kalay, Onur Can; Yüce, Celalettin; Doğan, Oğuz; Korcuklu, Burak
    Gears are critical power transmission elements used in various industries. However, varying working speeds and sudden load changes may cause root cracks, pitting, or missing tooth failures. The asymmetric tooth profile offers higher load-carrying capacity, long life, and the ability to lessen vibration than the standard (symmetric) profile spur gears. Gearbox faults that cannot be detected early may lead the entire system to stop or serious damage to the machine. In this regard, Deep Learning (DL) algorithms have started to be utilized for gear early fault diagnosis. This study aims to determine the root crack for both symmetric and asymmetric involute spur gears with a DL-based approach. To this end, single tooth stiffness of the gears was obtained with ANSYS software for healthy and cracked gears (50-100%), and then the time-varying mesh stiffness (TVMS) was calculated. A six-degrees-of-freedom dynamic model was developed by deriving the equations of motion of a single-stage spur gear mechanism. The vibration responses were collected for the healthy state, 50% and 100% crack degrees for both symmetric and asymmetric tooth profiles. Furthermore, the white Gaussian noise was added to the vibration data to complicate the early crack diagnosis task. The main contribution of this paper is that it adapts the DL-based approaches used for early fault diagnosis in standard profile involute spur gears to the asymmetric tooth concept for the first time. The proposed method can eliminate the need for large amounts of training data from costly physical experiments. Therefore, maintenance strategies can be improved by early crack detection.
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    Forensic Analysis of Digital Audio Recordings based on Acoustic Mains Hum
    (Ieee, 2016) Vatansever, Saffet; Dirik, Ahmet Emir
    ENF (Electrical Network Frequency), fluctuates instantaneously from its nominal value (50/60 Hz) depending on an increase or decrease in power consumption as against power production in the grid network. An ENF-sourced noise component is added into audio recordings where mains power sourced electromagnetic field or acoustic mains hum exists. With the use of this component, recording date and time of an audio file can be verified. In this work, existence and estimation of the acoustic mains hum sourced ENF noise in audio files is studied by analysing the acoustic noise emitted by several devices that are frequently used at home or in workplace. Detection of the file recording time truly is examined by computation of the similarity between the ENF signals estimated from the audio recordings and the reference ENF obtained with the help of a circuit that is connected to power grid network. The behaviour of dynamic microphone and electret microphone towards acoustic mains hum is investigated and the extent of acquiring the information about recording device type and settings from audio files is analysed. Besides, as part of this work, ENF estimation from videos on social media is also investigated.
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    VİDEOLARIN ENF TABANLI ADLİ KANIT ANALİZİNE IŞIK KAYNAĞI ETKİSİ
    (2017) Vatansever, Saffet; Dirik, Ahmet Emir
    Video dosyalarının Elektrik Şebeke Frekansı (Electric Network Frequency - ENF) temelli adli kanıt analizi tekniği, çoklu ortam dosyalarının kayıt zamanını doğrulamada ve dosyalarda yapılan sahteciliği tespit etmede son yıllarda önerilmiş en önemli araçlardan biridir. ENF, şebekede üretilen toplam gücün tüketilen toplam güce göre artıp azalmasına bağlı olarak nominal değer (Avrupa"da 50 Hz) etrafında sürekli salınımlar yapar. Bu salınımlar aynı şebeke üzerindeki her noktada aynıdır. Elektrik şebekesinden beslenen bir ışık kaynağının yaymış olduğu aydınlatma şiddeti elektrik şebeke frekansına bağlı olarak insan gözünün fark edemeyeceği anlık değişkenlikler gösterir. Işık şiddetindeki bu değişimler, video kameralar tarafından yakalanabilmektedir. Çekilen videolardaki tüm resim çerçeveleri boyunca değişmeyen içerik analiz edilerek aydınlatma şiddetinin değişim hızı, dolayısıyla elektrik şebeke frekansı kestirilebilir. Videolardan kestirimi yapılan ENF sinyalinin, elektrik şebekesinden doğrudan elde edilen referans ENF sinyali ile benzerlikleri hesaplanarak dosya kayıt zamanı bilgisine ulaşılabilir. Bu çalışmada, şebeke elektriğine bağlı ışık kaynağı türünün, CCD sensörlü kamera ile çekilmiş videolardan kestirilen ENF sinyali kalitesinde ne derece etkili olduğu incelenmiştir. Işık kaynağı türüne göre, çeşitli uzunluktaki videolarda ENF temelli kayıt zamanı doğrulama performansı analiz edilmiştir.