Security of TensorFlow includes significant issues such as TensorFlow models as programs, how to run untrusted models, and accepting the untrusted input.

TensorFlow Models as Programs

TensorFlow has its runtime system, which interprets and executes its programs. The program in TensorFlow is encoded as the computation graphs and stores the parameters separately as checkpoints.
During the runtime, TensorFlow executes the computation graph with the parameters given. The behavior of the chart may change depending upon the change in settings.
All of the tasks performed are done with the permission of the TensorFlow process.
The TensorFlow models are like the programs and therefore need to be taken as such from the TensorFlow security perspective.

Untrusted Models

It is a general that an untrusted model should first execute in a sandbox. There are many possibilities for any model to become untrusted, for instance, if some untrusted party provides TensorFlow kernel or provides python code required for TensorFlow graphs.
TensorFlow Security is determined by the computational graph, whether the user-provided checkpoint is safe or not. Generally, creating a computational graph with malicious inspections can trigger uncommon and insecure behavior.

Accepting the mistrustful input

Anyone can design models that are secure by providing the models to process the untrusted data safely.
We suppose that we have not any bugs.
An excellent way to analyze how the TensorFlow graph works, an interpreted programming language like Python. One can write a safe Python code that can easily expose to the user given inputs, but it is easy to write un-secure Python programs.

Vulnerabilities in TensorFlow

TensorFlow is an extensive complex system that depends on several third-party libraries for its use. TensorFlow model performs arbitrary computations that can read or write files or can communicate over a network. If the model shows other than these specifications, then that behavior can cause vulnerability.

Reporting a Vulnerability

We will see how to see the vulnerabilities in TensorFlow. We can send the reports about any issues regarding security to security@tensorflow.org. The report to the email is delivered to the security team in TensorFlow. The emails accept within 24 hours and provide a detailed response within a week, along with the next steps.

Difference between TensorFlow and Caffe

TensorFlow is an open-source python-based software library for numerical computation, which makes machine learning more accessible and faster using the data-flow graphs. TensorFlow eases the process of acquiring data-flow charts.
Caffe is a deep learning framework for training and running the neural network models, and vision and learning center develop it. TensorFlow relieves the process of acquiring data, predicting features, training many models based on the user data, and refining the future results. Caffe is designed with expression, speed, and modularity keep in mind.

Comparison between TensorFlow and Caffe

Basic	TensorFlow	Caffe
Definition	TensorFlow is used in the field of research and server products as both have a different set of targeted users.	Caffe is relevant for the production of edge deployment, where both structures have a different set of targeted users. Caffe desires for mobile phones and constrained platforms.
WLife Cycle management and API’s	TensorFlow offers high- level API’s for model building so that we can experiment quickly with TensorFlow API. It has a suitable interface for python language (which is a choice of language for data scientists) in machine learning jobs.	Caffe doesn’t have higher-level API due to which it will hard to experiment with Caffe, the configuration in a non-standard way with low-level APIs. The Caffe approach of middle-to-lower level API’s provides high-level support and limited deep setting. Caffe interface is more of C++, which means users need to perform more tasks manually, such as configuration file creation.
Easier Deployment	TensorFlow is simple to deploy as users need to install the python-pip manager easily, whereas, in Caffe, we have to compile all source files.	In Caffe, we don’t have straightforward methods to deploy. We need to compile each source code to implement it, which is a drawback.
GPU’s	In TensorFlow, we use GPU by using the tf.device () in which all necessary adjustments can make without any documentation and further need for API changes. In TensorFlow, we able to run two copies of the model on two GPUs and a single model on two GPUs.	In Caffe, there is no support of the python language. So all the training needs to be performed based on a C++ command-line interface. It supports a single layer of multi-GPU configuration, whereas TensorFlow supports multiple types of multi-GPU arrangements.
Multiple Machine support	In TensorFlow, the configuration is straightforward for multi-node tasks by setting the tf. Device to arrangement some posts, to run.	In Caffe, we need to use the MPI library for multi-node support, and it was initially used to break massive multi-node supercomputer applications.
Performance, the learning curve	The TensorFlow framework has less performance than Caffee in the internal comparing of Facebook. It has a sharp learning curve, and it works well on sequences and images. It is the most-used deep learning library along with Keras.	Caffe framework has a performance of 1 to 5 times more than TensorFlow in the internal benchmarking of Facebook. It works well for deep learning framework on images but not well on recurrent neural networks and sequence models.

Conclusion

Finally, we hope that a good understanding of these frameworks TensorFlow and Caffe. Tensorflow framework is the fast-growing and voted as most-used deep learning frameworks, and recently, Google has invested heavily in the framework. TensorFlow provides mobile hardware support, and low-level API core gives one end-to-end programming control and high-level API’s, which makes it fast and capable where Caffe backward in these areas compared to TensorFlow. So TensorFlow is more dominant in all deep learning frameworks.
So, this brings us to the end of blog. This Tecklearn ‘TensorFlow Security and TensorFlow Vs Caffe’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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• Python Programming Language
• Installation & IDE
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More Prerequisites for Deep Learning and AI
• Pandas for Data Analysis
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Multi-layered Neural Networks
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Regularization techniques in Deep Learning
Deep Learning Libraries
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Dual Equation Neural Network
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Introduction to Keras API
• Define Keras
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GPU in Deep Learning
• Introduction to GPUs and how they differ from CPUs
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Convolutional Neural Networks (CNN)
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