Are you grappling with the intricate challenge of tackling a sophisticated deep learning project focused on image detection, like the one outlined below? If you find yourself in need of expert guidance, particularly in the realm of building detection models for distinguishing real and fake images, CodersArts is here to provide the support you seek.
What Do We Offer?
At CodersArts, we understand the complexities involved in developing robust detection models, especially when dealing with datasets like the Deepfake detection dataset and the Synthetic image detection dataset. In this project, we embark on the journey of implementing a dataset and a dataloader for face images, training vanilla neural networks, and leveraging the Xception backbone for enhanced performance. Our comprehensive solution includes numerical and graphical metrics analysis, employing tools such as ROC curves, AuC, accuracy, and average precision. We also delve into image analysis using Class Activation Maps and Image-Specific Class Saliency Visualization, providing you with a thorough understanding of the model's performance.
Get Assignment Solution
To further assist you in mastering this project, we offer a sample assignment solution. If you require a solution for this assignment or face similar tasks, don't hesitate to reach out. CodersArts specializes in delivering comprehensive solutions for complex assignments in areas such as deep learning, image detection, and more. Contact us, and let us tailor our expertise to meet your specific needs.
1.1 The Problem:
You are given two datasets of real and fake images:
Deepfake detection dataset (“fakes dataset”)
Synthetic image detection dataset (“synthetic dataset”)
Each dataset contains a set of real images and a set of fake (/synthetic) images. The real images are images of existing identities. Fake images are created by planting one face into the context of another. Synthetic images are created by GANs trained against a pristine set of images.
1.2 Your Goal
Your goal is to build detection models which, given an image, predicts if the image is real or fake.
1.3 General Outline
Through the exercise we will :
Implement a dataset and a dataloader of face images.
Implement a generic trainer which will help us train a vanilla deep neural network and a deep neural network based on the Xception backbone.
We will succeed and fail (and finally succeed) training the aforementioned networks.
We will analyze classification results using both:
– Numerical and graphical metrics (ROC curves, AuC, accuracy, average precision...)
– Image analysis tools such as Class Activation Maps and Image-Specific Class Saliency Visualisation (Section 3 in link).
We will use Pytorch heavily in this exercise. We recommend that you use the Pytorch documentation whenever needed
1.4 The datasets
The Deepfake detection dataset contains fake images generated by taking a face of one subject and planting it in another subject’s context. This manipulation is applied using this github repository. The Synthetic ‘fake’ dataset contains images generated by PGAN where the discriminator is trained against images from the CelebA-HQ dataset.
The datasets are supplied in in Assigment4 datasets.zip file attached to the exercise. Extract the files. Your directory structure should look like in Figure 1.1
1.5 File Structure
common.py and utils.py : includes constants of repositories locations and utility functions to load models and datasets.
faces dataset.py : holds the faces dataset module which upon querying returns an image and a label.
show faces dataset.py : plots samples of the two datasets to a figure.
train main.py : the main training script. Among other arguments, it receives the model name and dataset name we wish to train.
trainer.py : an abstract training class. Takes the datasets and the model as inputs and trains the model with the train dataset. Evaluate and test the models performance with the corresponding datasets.
plot accuracy and loss.py : the trainer abstract class logs loss and accuracy to a json file. This script prints these metrics on graphs.
numerical analysis.py : loops through the test dataset and logs the scores each model computes, and prints ROC and DET curves and logs AuC performance score.
saliency map.py : compute the gradients of the correct class with respect to the image pixels to visualize the importance of each pixel.
grad cam analysis.py : compute the class activation map of an image with respect to some layer of the model
1.6 Downloading The Dataset
A link to the dataset is here. The link is restricted only to TAU students so make sure you’re logged in with the Tel-Aviv University account. If you do not have a TAU account, contact the TA.
Deliverables You Can Expect
At CodersArts, we ensure your success with assignments like the one showcased here. We will provide all the resources, expertise, and support you need to require this assignment. Our comprehensive solutions will include code implementations, basic project report with basic explanation, and any necessary documentation according to the requirement. Rest assured, we have you covered at every step of the way, ensuring your assignment is not just completed, but completed with excellence.
How We Can Help You Overcome Challenges
CodersArts offers tailored solutions to conquer the complexities of this project:
Expert Cloud Computing Guidance: Benefit from the expertise of seasoned cloud computing professionals who provide comprehensive guidance for tackling intricate tasks in image detection.
Efficient Data Processing: Learn techniques to efficiently acquire, store, and preprocess extensive datasets, optimizing your data computation tasks for superior performance.
Error Handling: Receive timely assistance in debugging and resolving issues that might arise during the development and execution of your image detection solution.
Tailored Support: We provide one-on-one support tailored to your specific project needs, ensuring you have the resources and guidance necessary to succeed.
Why Choose CodersArts Expertise
Experienced Team: Our team comprises seasoned experts in deep learning and image detection with a wealth of industry experience.
Tailored Solutions: We customize our support to your proficiency level and the unique demands of your project, ensuring a perfect fit for your requirements.
Timely Support: We recognize the importance of meeting deadlines, and we provide swift assistance to keep your project on track.
In-Depth Understanding: Our commitment goes beyond completing your assignment; we make sure you thoroughly comprehend the core concepts of deep learning and image detection.
Affordability: We offer competitive pricing, making expert guidance in deep learning and image detection within reach for all.
If you're in search of a solution for this assignment or require assistance with similar projects, feel free to get in touch with us via email at email@example.com. Your success is our priority, and we look forward to being your trusted partner in conquering complex assignments and projects. Reach out to us today and experience the difference CodersArts can make in your academic and professional journey.