Gears are fundamental components used to transmit power and motion in modern industry. Their health condition monitoring is crucial to ensure reliable operations, prevent unscheduled shutdowns, and ...minimize human casualties. From this standpoint, the present study proposed a one-dimensional convolutional neural network (1-D CNN) model to diagnose tooth root cracks for standard and asymmetric involute spur gears. A 6-degrees-of-freedom dynamic model of a one-stage spur gear transmission was established to achieve this end and simulate vibration responses of healthy and cracked (25%–50%–75%–100%) standard (20°/20°) and asymmetric (20°/25° and 20°/30°) spur gear pairs. Three levels of signal-to-noise ratios were added to the vibration data to complicate the early fault diagnosis task. The primary consideration of the present study is to investigate the asymmetric gears’ dynamic characteristics and whether tooth asymmetry would yield an advantage in detecting tooth cracks easier to add to the improvements it affords in terms of impact resistance, bending strength, and fatigue life. The findings indicated that the developed 1-D CNN model’s classification accuracy could be improved by up to 12.8% by using an asymmetric (20°/30°) tooth profile instead of a standard (20°/20°) design.
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 superpixels, 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.
Due to a constant imbalance between demand and supply of power, ENF (Electric Network Frequency) fluctuates around a nominal value of 50 or 60 Hz. These variations in ENF cause the luminance ...intensity of a mains-powered light source, having no AC/DC converter inside, also to fluctuate. As a result, a video of a scene illuminated by a mains-powered light source can be used to estimate these fluctuations. As a consequence, the ENF signal within the time period when the video was captured can be estimated. This work explores the effects of frame rate harmonics that emerge when a rolling shutter based approach is used for ENF estimation from videos captured using CMOS cameras. These harmonics are a problem, especially for videos whose frame rate is a divisor of the nominal ENF because the frame rate harmonics and the ENF harmonics overlap. It is discovered that a key reason for the presence of the harmonics is the inverse square law of light that results in some repeating patterns of luminance variation across frames. This paper presents an analysis of the effect of the inverse square law of light on ENF estimation. A technique for refined ENF-related luminance signal estimation is proposed that attenuates these frame rate harmonics. This enables more accurate ENF estimates. The work also proposes an approach to estimate ENF-related luminance waveform cycles within each video frame, and a method to compute the confidence score for the estimated cycles. It provides insight into the reliability of the extracted ENF signal from a video, in the sense of its usefulness for ENF forensics, and consequently for ENF detection, which is an important precursor to ENF-based video forensics.
ENF Based Robust Media Time-Stamping Vatansever, Saffet; Dirik, Ahmet Emir; Memon, Nasir
IEEE signal processing letters,
2022, Volume:
29
Journal Article
Peer reviewed
Electric Network Frequency (ENF) continuously fluctuates around a nominal value (50/60 Hz) due to a persistent imbalance between supplied and demanded power. In certain circumstances, ENF gets ...intrinsically embedded into audio and video recordings and can be extracted from these recordings. Consequently, ENF can be used in a number of media forensic applications, such as verifying the time of recording of the media. In this work, a robust media time-stamping approach is proposed for media whose ENF content is relatively contaminated. It essentially entails two procedures: first, detecting all useful, i.e., considerably accurate, samples of an estimated ENF signal, and then applying an adapted normalized cross-correlation process that is designed for exploiting just the selected ENF portions based on a binary mask of the identified accurate samples. Experimental results show that the proposed approach provides significantly increased performance.
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) due 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 be captured by video recordings. Accordingly, the ENF can be 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.
Perturbed quantization (PQ) data hiding is almost undetectable with the current steganalysis methods. We briefly describe PQ and propose singular value decomposition (SVD)-based features for the ...steganalysis of JPEG-based PQ data hiding in images. We show that JPEG-based PQ data hiding distorts linear dependencies of rows/columns of pixel values, and proposed features can be exploited within a simple classifier for the steganalysis of PQ. The proposed steganalyzer detects PQ embedding on relatively smooth stego images with 70% detection accuracy on average for different embedding rates
Analogous to use of bullet scratches in forensic science, the authenticity of a digital image can be verified through the noise characteristics of an imaging sensor. In particular, photo-response ...non-uniformity noise (PRNU) has been used in source camera identification (SCI). However, this technique can be used maliciously to track or inculpate innocent people. To impede such tracking, PRNU noise should be suppressed significantly. Based on this motivation, we propose a counter forensic method to deceive SCI. Experimental results show that it is possible to impede PRNU-based camera identification for various imaging sensors while preserving the image quality.
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.
The availability of sophisticated source attribution techniques raises new concerns about privacy and anonymity of photographers, activists, and human right defenders who need to stay anonymous while ...spreading their images and videos. Recently, the use of seam-carving, a content-aware resizing method, has been proposed to anonymize the source camera of images against the well-known photoresponse nonuniformity (PRNU)-based source attribution technique. In this paper, we provide an analysis of the seam-carving-based source camera anonymization method by determining the limits of its performance introducing two adversarial models. Our analysis shows that the effectiveness of the deanonymization attacks depend on various factors that include the parameters of the seam-carving method, strength of the PRNU noise pattern of the camera, and an adversary's ability to identify uncarved image blocks in a seam-carved image. Our results show that, for the general case, there should not be many uncarved blocks larger than the size of 50\times 50 pixels for successful anonymization of the source camera.
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