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A fun picture one from me this time. I have the model's consent to use her pictures in this scene. She's a genuine, part-time model from Bedfordshire, England, similar to Melody to as I depicted in the piece -- although she doesn't take it as far as Melody does, this is a work of fiction after all.
OK, in this scene a young man sees a woman he's sure is an internet model. He approaches her obliquely and, when challenged, admits to perusing her nude pictures on her website. Remarkably the model invites him to visit her at home and take a few pictures for himself.
Besides, I was meant to be working, stacking shelves in Sainsbury's supermarket -- a summer job before university started -- but nevertheless I still found an excuse to walk past her once ... then twice ... and yet a third time. Just to confirm what I already knew. It was her -- the model, MILF Melody.
\"That's probably because you have,\" she said, as casual as you like, as though most women got their kit off and showed their big boobs and plump-lipped pussy on their own website. \"I model part-time. I've got a career as well but I've modelled for years.\" Melody grinned at me, adding, \"So, what do you think then You've seen the pictures. What do you reckon\"
The model was wearing a white, short-sleeved blouse with a faint jig-zag pattern. A skirt with a ruffled hem and floral pattern fell to a point a couple of inches above her knees, while white shoes completed the ensemble. As she chattered away, telling me about her family, modelling career and, surprisingly, her day job as an accountant of all things, I couldn't help but drift away on a reverie of the previous night's masturbatory delight.
I gulped and stared at the bulge of Melody's pudendum before, after a gentle reminder from the model, I took the picture. And so it went on. My MILF model would strike a pose and I'd point the camera at her and take the shot. It was an excruciatingly tantalising strip, with Melody suggestively posing and slowly, ever so teasingly, revealed her body to me. My penis was stiff inside my jeans, pre-cum dribbling from its eye, and I could hear my own heartbeat solid and thumping in my ears as I pointed and clicked, pointed and clicked.
\"Oh fuck,\" I sighed. \"Oh fucking hell ...\" Melody had swivelled on the seat and eased her hips off the sofa. What I now saw before me was the model with legs akimbo and the slit of her pussy visible. Melody's underwear was a taut bowstring around one ankle and her other shoe. \"Ah, shit ...\" I groaned while, holding her breasts in her fingers, Melody regarded me through the frame of her panties and upraised V of her legs.
\"All right,\" Melody said. \"I think you probably got a hundred pictures there. She walked over to me as though nudity was completely usual. Uninhibited by her nakedness, Melody stood close by my side, so close I could smell her perfume and feel the heat of her body. Melody peered at the screen on the camera's inverse side. \"Let's have a look at a couple, she said.\" I pressed the buttons and Melody perused a few frames. \"What do you reckon\" the model asked after handing the camera back to me. \"Will you be using them later on ... in private\" She winked at me and I felt the heat rise in my face.
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Jayne's son Gabriel was also a contestant on MILF Manor. His bio on the show's site reads, \"Gabriel is a model and a singer. He lives in Los Angeles and has a twin brother. Gabriel, his brother, and their father have a rock band together, and they regularly perform at clubs on Sunset Blvd. He is a huge flirt and has a hard time being serious in relationships. Will all of that change with one of the ladies in the villa\"
Deep learning-based models can be powerful tools for learning the features of training data and capturing the normal behavior of a normal data distribution. However, one major issue originating from an intrinsic attribute of DNNs is their ability to be affected by the input data. Because of their sensitivity to small perturbances, DNNs may be misled and misclassify images with a certain number of imperceptible perturbations [2]. As a result, when poison or non-ideal sample distributions are present in the testing data, features learned by the deep CNN may not be robust. Unfortunately, producing ideal samples is difficult in real-world data. As an example, in the steel production process, producing an ideal image is not easy due to the harsh work environment; moreover, capturing images in extremely high temperatures makes them very vulnerable to noise. In order to optimize the performance of anomaly detection models with poison samples, a strong and adaptive model is needed.
Today, deep learning-based anomaly image detection is used in many industries, including steel production. Currently, CNN methods such as VGG [3] and ResNet [4] are becoming de facto approaches for extracting the features for many anomaly detection problems [5,6,7]. A common way to implement deep learning-based models is to learn the feature representation of normality as presented in the survey paper [8], where the deep learning model is guided to search for the important features from normal data and defect data. Most of the models in the steel production industry use a technique to classify product defects, such as cracks, scratches, markings, missing parts, and inaccuracies in various inspection tasks [9,10]. These CNN-based models perform well when trained on vast amounts of ideal data. However, data subjected to noise, known as poison data, is a hindrance to the success of deep network-based anomaly detection systems in real-world applications. The original training data also do not cover the full range of possible future anomaly or defect classes. Within this reality, data scarcity is not the only constraint we face; it is also crucial to obtain additional ideal representative images.
Ideal or uniform samples can be defined as similar data variations that typically share the same conditions, such as image quality, exposure, and lighting conditions. Currently, the existing methods [11] for industrial anomaly detection are generally suitable for uniform images that possess the ideal conditions. However, in practice, the distribution of testing images from industrial image collections can vary in terms of quality and conditions. In this situation, the testing samples may include random quality of samples that obstruct the success of current models.
This work mainly focuses on anomaly detection in an industrial manufacturing inspection context. To address non-uniform data in this domain, we design a novel additional module that explores the benefit of robust image transformations to introduce variation into existing normal images. At the same time, a combination of multi-level features is added to a multivariate Gaussian distribution model to enhance the normality learning process. Our proposed method, Hierarchical Image Transformation and Multi-level Features (HIT-MiLF), explores the new additional transformation samples and improves the relationship of high-dimensional features of CNN. Our approach can be viewed as an additional module to assist in the feature extraction process for non-ideal or poison images. Unlike other works, our method exploits the model to learn more variety from normal images rather than introducing variety into normal images for anomaly detection. We prove that the model becomes more sensitive to perturbances in testing samples for both normal and defect samples. Our method also achieved higher prediction scores on test images with various poison levels compared to a model without HIT-MiLF.
Hierarchical image transformation (HIT) module and multi-level features (MiLF) added to an anomaly detection model. Dn represents input training data from a one-class normal image; the output of the model is normal (Dn) or anomaly (Da).
However, in the complex condition of normal samples, high-dimensional features from a deep neural network cannot fully describe the normality feature of training data. This is because there is a lack of variation among limited anomaly-free training data. Therefore, it is crucial to enlarge the data in order to enrich the variation and strengthen the data complexity. To address this issue, apart from generating new samples in the single HIT module, we propose a combined model that jointly uses HIT and multi-level features from ResNet18 to extract variations of anomaly-free images. We utilize the multi-level features from different blocks of ResNet18 to capture the different relationship and semantic information features from normal samples. 59ce067264
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