#guidance for compliance

[alexander-clifford]

Breaking down HMRC's guidelines for compliance for a successful R&D claim

R&D legislation is always evolving to prevent fraud and to ensure that the incentive covers all types of costs for innovative businesses carrying out research and development. HMRC has developed an informative resource known as the Guidelines for Compliance (GfC3) to make a successful R&D claim. The purpose of the guidance is to help you avoid common mistakes in claiming R&D tax credits to increase the chances of a successful application. In this article, we will outline the key R&D tax relief guidance to function as your handy guide, helping you to provide HMRC with what they are looking for in your claim, as well as filling in the blanks where confusion sits.

What are HMRC’s latest guidelines for compliance for R&D?

HMRC’s guidelines for compliance were first published in 2021 and have been evolving since then. We see this guidance as an essential resource for R&D claimants. Over the last few years, HMRC has increased their vigilance over fraud and errors in R&D claims, even employing extra inspectors to monitor the compliance levels of R&D tax credit claims. They write in part one: “We want to help you make a correct R&D claim, avoiding the penalty we charge if carelessness leads to an inaccurately high claim.” The guidance they've offered aids in reducing the likelihood of an inquiry which is made up of 6 parts that cover:

Part 1: Purpose, scope and background

This details what to expect from the following parts. The guidance summarises best practices for claiming R&D tax credits and provides key information on: 

Understanding what HMRC expects of you as the role of the claimant. 

How to identify who is a ‘competent professional’ in the eyes of HMRC.

Knowing if your work qualifies as R&D. 

Pinpointing what they mean by ‘scientific or technological advancement’ as this is a central component of the R&D eligibility criteria. 

Detailing the scientific or technological advancement of your project and knowing how it counts as an advancement.

The importance of identifying exactly when your R&D project began and finished. This requires an understanding of what counts as R&D.

Knowing what evidence and documentation is required for your R&D claim.

Part 2: Expectations of claimants

This part of the guidance is all about what you need to do before you make the R&D claim. HMRC provides 13 steps that you need to take. Examples include that the competent professional should be responsible for: 

Identifying the R&D projects you're claiming for (they must have the aim to create an advance past existing knowledge and your competitors’ capabilities). 

Outlining the beginning of each uncertainty and at which point it was solved. If it wasn’t solved in the year you’re claiming, make that clear. 

Describing what the plan was to tackle the uncertainty. 

Validating if the R&D costs align with the eligibility criteria.

Part 3: Importance of a competent professional

A common error that arises in R&D claims is the incorrect identification of the competent professional. You’ll need to consult with this individual for your claim to gain their insights regarding the R&D projects. A competent professional is someone with relevant experience or qualifications in the specific field of the R&D project you're engaged in. Perhaps they have industry awards, a degree, continuous professional development in the field, or a relevant publication in the field, as examples, and this would make them a competent professional. Having a competent professional involved in your R&D claim serves the purpose of utilising their expertise to identify uncertainty and technological advancement.

Part 4: How to identify qualifying R&D activities

When we discuss an R&D project, what we mean is a series of R&D activities and HMRC provides extensive information on what counts as eligible R&D activities in part 4. Each of these activities works together collectively to reach a goal. A project is a necessity - a common error that keeps arising is when a business has advanced in science and technology, but if it has not been as part of a project then it can’t count for R&D tax credits. If the project just had purely commercial aims as opposed to actually resolving technical or scientific uncertainties, then it doesn’t qualify. It can’t use readily available information either. 

Read about the examples of R&D projects. In simple terms, an R&D project may seek an advancement by creating or improving a: 

Process

Material

Device

Product

Service

Part 5: The recommended approach to R&D claims and record keeping

A lot of documentation goes into each R&D claim and this involves extensive record keeping. The main point HMRC iterates is that if you believe the work you’re doing is eligible R&D then you should plan for your R&D claim by writing as you go along. We recommend you journal your activities, including the failures, because it will strengthen your technical narrative being able to see exactly how you made the technical advancement and the key dates involved. Keep all receipts and invoices to help you create a claim with financial accuracy. 

Of course, it is common that you may identify eligible R&D activities after the project has been completed. We often find this with our clients because we reach out to them to check if their innovative project qualifies. This can often make the documentation difficult because you have to cast your mind back to the little details of the project. A competent professional or R&D tax credit specialist such as ourselves can help you determine eligibility when looking back.

More records will be needed if HMRC decides to open a compliance check into your claim. Essentially, they’ll need to see some evidence. This could look like: 

Seeing requesting documents 

Visiting your site 

Examining the completed products or prototypes 

Speaking to your team

Part 6: Further information

In this part of the R&D tax relief guidance, HMRC details what to do if you have made an error in an earlier R&D claim as well as other key pieces of information and key contact details. 

How should you use this guidance?

These guidelines for compliance from HMRC are incredibly helpful for all R&D claimants; however, without an in-depth understanding of R&D legislation, there is still scope for error which needs to be avoided in light of HMRC’s crackdown on non-compliance. In other words, it is not a substitute. Professional R&D advice is always recommended to accompany this guidance for your claim.

What are the latest changes to R&D tax credit relief in 2024?

This guidance was almost overshadowed by the changes to R&D tax credits that were announced in the Autumn Statement 2023 and will commence after the 1st of April 2024. Here’s a recap of what they are:

There are no longer separate R&D tax credit schemes based on the size of your company and expenditure. The 2 schemes have merged into one with the same rate of 20%, seen as an above-the-line benefit. 

The rate for loss-making companies is 25%. 

The R&D expenditure threshold has been reduced to 30%.

There is a one-year grace period if you don’t meet the 30% intensity threshold. 

Payments will no longer be sent to third parties.

There is still a requirement to notify HMRC if you are claiming for the first time and to fill out an R&D additional information form.

Key takeaways

The recently issued R&D tax relief guidance by HMRC is an information-dense resource that serves as a helping hand for R&D claimants. It focuses on the importance of properly preparing your claim with a correctly chosen competent professional and knowing about what qualifies as an R&D activity. Understanding this guidance enables you to provide HMRC with the exact information they need, speeding up the process of receiving your tax benefits directly and reducing the chance of undergoing a time-consuming (and sometimes costly) compliance check.

How can Alexander Clifford help you? 

As R&D tax credit specialists with a long track record of successful R&D claims, our experts understand the intricacies of R&D legislation and use HMRC’s R&D tax relief guidance in our processes for each R&D claim. Whether you’re ready to start your claim or you want to check if you’re eligible, our team is here to help. Get in touch with our team of sector specialists. 

[intertaxtrade]

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[fusion-compliance-services]

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[jcmarchi]

Extracting Training Data From Fine-Tuned Stable Diffusion Models

New Post has been published on https://thedigitalinsider.com/extracting-training-data-from-fine-tuned-stable-diffusion-models/

Extracting Training Data From Fine-Tuned Stable Diffusion Models

New research from the US presents a method to extract significant portions of training data from fine-tuned models.

This could potentially provide legal evidence in cases where an artist’s style has been copied, or where copyrighted images have been used to train generative models of public figures, IP-protected characters, or other content.

From the new paper: original training images are seen in the row above, and the extracted images are depicted in the row below. Source: https://arxiv.org/pdf/2410.03039

Such models are widely and freely available on the internet, primarily through the enormous user-contributed archives of civit.ai, and, to a lesser extent, on the Hugging Face repository platform.

The new model developed by the researchers is called FineXtract, and the authors contend that it achieves state-of-the-art results in this task.

The paper observes:

‘[Our framework] effectively addresses the challenge of extracting fine-tuning data from publicly available DM fine-tuned checkpoints. By leveraging the transition from pretrained DM distributions to fine-tuning data distributions, FineXtract accurately guides the generation process toward high-probability regions of the fine-tuned data distribution, enabling successful data extraction.’

Far right, the original image used in training. Second from right, the image extracted via FineXtract. The other columns represent alternative, prior methods. Please refer to the source paper for better resolution.

Why It Matters

The original trained models for text-to-image generative systems as Stable Diffusion and Flux can be downloaded and fine-tuned by end-users, using techniques such as the 2022 DreamBooth implementation.

Easier still, the user can create a much smaller LoRA model that is almost as effective as a fully fine-tuned model.

An example of a trained LORA, offered for free download at the hugely popular civitai domain. Such a model can be created in anything from minutes to a few hours, by enthusiasts using locally-installed open source software – and online, through some of the more permissive API-driven training systems. Source: civitai.com

Since 2022 it has been trivial to create identity-specific fine-tuned checkpoints and LoRAs, by providing only a small (average 5-50) number of captioned images, and training the checkpoint (or LoRA) locally, on an open source framework such as Kohya ss, or using online services.

This facile method of deepfaking has attained notoriety in the media over the last few years. Many artists have also had their work ingested into generative models that replicate their style. The controversy around these issues has gathered momentum over the last 18 months.

The ease with which users can create AI systems that replicate the work of real artists has caused furor and diverse campaigns over the last two years. Source: https://www.technologyreview.com/2022/09/16/1059598/this-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it/

It is difficult to prove which images were used in a fine-tuned checkpoint or in a LoRA, since the process of generalization ‘abstracts’ the identity from the small training datasets, and is not likely to ever reproduce examples from the training data (except in the case of overfitting, where one can consider the training to have failed).

This is where FineXtract comes into the picture. By comparing the state of the ‘template’ diffusion model that the user downloaded to the model that they subsequently created through fine-tuning or through LoRA, the researchers have been able to create highly accurate reconstructions of training data.

Though FineXtract has only been able to recreate 20% of the data from a fine-tune*, this is more than would usually be needed to provide evidence that the user had utilized copyrighted or otherwise protected or banned material in the production of a generative model. In most of the provided examples, the extracted image is extremely close to the known source material.

While captions are needed to extract the source images, this is not a significant barrier for two reasons: a) the uploader generally wants to facilitate the use of the model among a community and will usually provide apposite prompt examples; and b) it is not that difficult, the researchers found, to extract the pivotal terms blindly, from the fine-tuned model:

Essential keywords can usually be extracted blindly from the fine-tuned model using an L2-PGD attack over 1000 iterations, from a random prompt.

Users frequently avoid making their training datasets available alongside the ‘black box’-style trained model. For the research, the authors collaborated with machine learning enthusiasts who did actually provide datasets.

The new paper is titled Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data, and comes from three  researchers across Carnegie Mellon and Purdue universities.

Method

The ‘attacker’ (in this case, the FineXtract system) compares estimated data distributions across the original and fine-tuned model, in a process the authors dub ‘model guidance’.

Through ‘model guidance’, developed by the researchers of the new paper, the fine-tuning characteristics can be mapped, allowing for extraction of the training data.

The authors explain:

‘During the fine-tuning process, the [diffusion models] progressively shift their learned distribution from the pretrained DMs’ [distribution] toward the fine-tuned data [distribution].

‘Thus, we parametrically approximate [the] learned distribution of the fine-tuned [diffusion models].’

In this way, the sum of difference between the core and fine-tuned models provides the guidance process.

The authors further comment:

‘With model guidance, we can effectively simulate a “pseudo-”[denoiser], which can be used to steer the sampling process toward the high-probability region within fine-tuned data distribution.’

The guidance relies in part on a time-varying noising process similar to the 2023 outing Erasing Concepts from Diffusion Models.

The denoising prediction obtained also provide a likely Classifier-Free Guidance (CFG) scale. This is important, as CFG significantly affects picture quality and fidelity to the user’s text prompt.

To improve accuracy of extracted images, FineXtract draws on the acclaimed 2023 collaboration Extracting Training Data from Diffusion Models. The method utilized is to compute the similarity of each pair of generated images, based on a threshold defined by the Self-Supervised Descriptor (SSCD) score.

In this way, the clustering algorithm helps FineXtract to identify the subset of extracted images that accord with the training data.

In this case, the researchers collaborated with users who had made the data available. One could reasonably say that, absent such data, it would be impossible to prove that any particular generated image was actually used in training in the original. However, it is now relatively trivial to match uploaded images either against live images on the web, or images that are also in known and published datasets, based solely on image content.

Data and Tests

To test FineXtract, the authors conducted experiments on few-shot fine-tuned models across the two most common fine-tuning scenarios, within the scope of the project: artistic styles, and object-driven generation (the latter effectively encompassing face-based subjects).

They randomly selected 20 artists (each with 10 images) from the WikiArt dataset, and 30 subjects (each with 5-6 images) from the DreamBooth dataset, to address these respective scenarios.

DreamBooth and LoRA were the targeted fine-tuning methods, and Stable Diffusion V1/.4 was used for the tests.

If the clustering algorithm returned no results after thirty seconds, the threshold was amended until images were returned.

The two metrics used for the generated images were Average Similarity (AS) under SSCD, and Average Extraction Success Rate (A-ESR) – a measure broadly in line with prior works, where a score of 0.7 represents the minimum to denote a completely successful extraction of training data.

Since previous approaches have used either direct text-to-image generation or CFG, the researchers compared FineXtract with these two methods.

Results for comparisons of FineXtract against the two most popular prior methods.

The authors comment:

‘The [results] demonstrate a significant advantage of FineXtract over previous methods, with an improvement of approximately 0.02 to 0.05 in AS and a doubling of the A-ESR in most cases.’

To test the method’s ability to generalize to novel data, the researchers conducted a further test, using Stable Diffusion (V1.4), Stable Diffusion XL, and AltDiffusion.

FineXtract applied across a range of diffusion models. For the WikiArt component, the test focused on four classes in WikiArt.

As seen in the results shown above, FineXtract was able to achieve an improvement over prior methods also in this broader test.

A qualitative comparison of extracted results from FineXtract and prior approaches. Please refer to the source paper for better resolution.

The authors observe that when an increased number of images is used in the dataset for a fine-tuned model, the clustering algorithm needs to be run for a longer period of time in order to remain effective.

They additionally observe that a variety of methods have been developed in recent years designed to impede this kind of extraction, under the aegis of privacy protection. They therefore tested FineXtract against data augmented by the Cutout and RandAugment methods.

FineXtract’s performance against images protected; by Cutout and RandAugment.

While the authors concede that the two protection systems perform quite well in obfuscating the training data sources, they note that this comes at the cost of a decline in output quality so severe as to render the protection pointless:

Images produced under Stable Diffusion V1.4, fine-tuned with defensive measures – which drastically lower image quality. Please refer to the source paper for better resolution.

The paper concludes:

‘Our experiments demonstrate the method’s robustness across various datasets and real-world checkpoints, highlighting the potential risks of data leakage and providing strong evidence for copyright infringements.’

Conclusion

2024 has proved the year that corporations’ interest in ‘clean’ training data ramped up significantly, in the face of ongoing media coverage of AI’s propensity to replace humans, and the prospect of legally protecting the generative models that they themselves are so keen to exploit.

It is easy to claim that your training data is clean, but it’s getting easier too for similar technologies to prove that it isn’t – as Runway ML, Stability.ai and MidJourney (amongst others) have found out in recent days.

Projects such as FineXtract are arguably portents of the absolute end of the ‘wild west’ era of AI, where even the apparently occult nature of a trained latent space could be held to account.

* For the sake of convenience, we will now assume ‘fine-tune and LoRA’, where necessary.

First published Monday, October 7, 2024

[freyrnigeria]

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“Reject All” cookie consent update

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ICAI's Guidance Note on Audit of Banks (2024 Edition)

The Institute of Chartered Accountants of India (ICAI) stands at the forefront of shaping the accounting profession, providing guidance and standards to ensure excellence and integrity in financial practices. as usual on February 14, 2024, ICAI issued the latest edition of its Guidance Note on Audit of Banks, offering comprehensive insights and directives for auditors navigating the complex…

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Navigating the UK Sponsorship Licence Process for Skilled Workers

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Conclusion

In conclusion, VAT services play a critical role in ensuring businesses meet their tax obligations while taking advantage of available deductions and benefits. George M Kendall's team of expert VAT consultants offers a comprehensive range of services, from VAT registration to specialized VAT advice. By entrusting your VAT needs to professionals, you can save time, money, and effort while maintaining compliance with the ever-changing tax landscape. For businesses looking to streamline their financial operations and maximize tax efficiency, VAT services provided by George M Kendall are a valuable investment. Contact us!

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Navigating the Complexities: Expert Business Tax Compliance Guidance Services

Tax compliance can be daunting for businesses, especially with ever-changing regulations and complex procedures. That's where expert guidance services come into play. Professionals in Business Tax Compliance Guidance Services can help you navigate the intricacies and ensure adherence to all legal requirements.

Their expertise extends beyond basic tax preparation. These specialists provide comprehensive support, covering areas such as filing accurate returns, maximizing deductions, and minimizing liabilities. With their in-depth knowledge of the tax landscape, they can offer strategic advice tailored to your business needs.

Expert guidance services also keep you updated on the latest changes in tax laws, helping you stay compliant and avoid penalties. They provide invaluable insights into intricate tax provisions, helping you navigate complexities that could otherwise be overwhelming.

Partnering with these professionals, businesses can focus on core operations, knowing that their tax matters are handled meticulously. By leveraging their experience and expertise, you can optimize your tax position, mitigate risks, and ensure compliance with confidence.

Regarding tax compliance, relying on expert guidance services is a wise investment. It ensures accuracy, minimizes risks, and helps your business thrive in a complex tax environment.

DialTax is a leading tax consulting firm offering comprehensive business tax services. With a team of experienced professionals, they provide expert guidance and compliance solutions to navigate the complexities of tax regulations. Their tailored approach and commitment to accuracy make them a trusted partner for businesses seeking seamless tax management.