#FactCheck - Afghan Cricket Team's Chant Misrepresented in Viral Video

Introduction:

Welcome to the second edition of our blog on Digital forensics series. In our previous blog we discussed what digital forensics is,  the process followed by the tools, and the subsequent challenges faced in the field. Further, we looked at how the future of Digital Forensics will hold in the current scenario.  Today, we will explore differences between 3 particular similar sounding terms that vary significantly in functionality when implemented: Copying, Cloning and Imaging.

In Digital Forensics, the preservation and analysis of electronic evidence are important for investigations and legal proceedings. Replication of the data and devices is one of the fundamental tasks in this domain, without compromising the integrity of the original evidence. 

Three primary techniques -- copying, cloning, and imaging -- are used for this purpose. Each technique has its own strengths and is applied according to the needs of the investigation.

In this blog, we will examine the differences between copying, cloning and imaging. We will talk about the importance of each technique, their applications and why imaging  is considered the best for forensic investigations.

Copying

Copying means duplicating data or files from one location to another. When one does copying, it implies that one is using standard copy commands. However, when dealing with evidence, it might be hard to use copy only. It is because the standard copy can alter the metadata and change the hidden or deleted data .

 The characteristics of copying include: 

• Speed: copying is simpler and faster,compared to cloning or imaging.
•  Risk: The risk involved in copying is that the metadata might be altered and all the data might be captured.

Cloning

It is the process where the transfer of the entire contents of a hard drive or a storage device is done  on another storage device. This process is known as cloning . This way, the cloning process captures both the active data and the unallocated space and hidden partitions, thus containing the whole structure of the original device. Cloning is generally used at the sector level of the device. Clones can be used as the working copy of a device .

Characteristics of cloning: 

•  bit-for-bit replication: cloning keeps the exact content and the whole structure of the original device. 
• Use cases: cloning is used when it is needed to keep the original device intact for further examination or a legal affair. 
•  Time consuming: Cloning is usually longer in comparison to simple copying since it involves the whole detailed replication. Though it depends on various factors like the size of the storage device, the speed of the devices involved, and the method of cloning.

Imaging:

It is the process of creating a forensic image of a storage device. A forensic image is a replica copy of every bit of data that was on the source device, this including the allocated, unallocated, and the available slack space .

 The image is then used for analysis and investigation, and the original evidence is left untouched. Images can’t be used as the working copies of a device.  Unlike cloning, which produces working copies, forensic images are typically used for analysis and investigation purposes and are not intended for regular use as working copies.

Characteristics of Imaging:

•  Integrity: Imaging ensures the integrity and authenticity of the evidence produced
•  Flexibility: Forensic image replicas can be mounted as a virtual drive to create image-specific mode for analysis of data without affecting the original evidence . 
• Metadata: Imaging captures metadata associated with the data, thus promoting forensic analysis.

Key Differences

• Purpose: Copying is for everyday use but not good for forensic investigations requiring data integrity. Cloning and imaging are made for forensic preservation.
• Depth of Replication: Cloning and imaging captures the entire storage device including hidden, unallocated, and deleted data  whereas copying may miss crucial forensic data.
• Data Integrity: Imaging and cloning keep the integrity of the original evidence thus making them suitable for legal and forensic use. Which is a critical aspect of forensic investigations.
• Forensic Soundness: Imaging is considered the best in digital forensics due to its comprehensive and non-invasive nature.
• Cloning is generally from one hard disk to another, where as imaging creates a compressed file that contains a snapshot of the entire hard drive or a specific partitions

Conclusion

Therefore, copying, cloning, and imaging all deal with duplication of data or storage devices with significant variations, especially in digital forensic. However, for forensic investigations, imaging is the most selected approach due to the correct preservation of the evidence state for any analysis or legal use . Therefore, it is essential for forensic investigators to understand these rigorous differences to avail of real and uncontaminated digital evidence for their investigation and legal argument.

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Introduction

The Senate bill introduced on 19 March 2024 in the United States would require online platforms to obtain consumer consent before using their data for Artificial Intelligence (AI) model training. If a company fails to obtain this consent, it would be considered a deceptive or unfair practice and result in enforcement action from the Federal Trade Commission (FTC) under the AI consumer opt-in, notification standards, and ethical norms for training (AI Consent) bill. The legislation aims to strengthen consumer protection and give Americans the power to determine how their data is used by online platforms. 

The proposed bill also seeks to create standards for disclosures, including requiring platforms to provide instructions to consumers on how they can affirm or rescind their consent. The option to grant or revoke consent should be made available at any time through an accessible and easily navigable mechanism, and the selection to withhold or reverse consent must be at least as prominent as the option to accept while taking the same number of steps or fewer as the option to accept.

The AI Consent bill directs the FTC to implement regulations to improve transparency by requiring companies to disclose when the data of individuals will be used to train AI and receive consumer opt-in to this use. 
The bill also commissions an FTC report on the technical feasibility of de-identifying data, given the rapid advancements in AI technologies, evaluating potential measures companies could take to effectively de-identify user data.

The definition of ‘Artificial Intelligence System’ under the proposed bill

ARTIFICIALINTELLIGENCE SYSTEM- The term artificial intelligence system“ means a machine-based system that—

1. Is capable of influencing the environment by producing an output, including predictions, recommendations or decisions, for a given set of objectives; and
2. 2. Uses machine or human-based data and inputs to

(i) Perceive real or virtual environments;

(ii) Abstract these perceptions into models through analysis in an automated manner (such as by using machine learning) or manually; and

(iii) Use model inference to formulate options for outcomes.

Importance of the proposed AI Consent Bill USA

1. Consumer Data Protection: The AI Consent bill primarily upholds the privacy rights of an individual. Consent is necessitated from the consumer before data is used for AI Training; the bill aims to empower individuals with unhinged autonomy over the use of personal information. The scope of the bill aligns with the greater objective of data protection laws globally, stressing the criticality of privacy rights and autonomy.

2. Prohibition Measures: The proposed bill intends to prohibit covered entities from exploiting the data of consumers for training purposes without their consent. This prohibition extends to the sale of data, transfer to third parties and usage. Such measures aim to prevent data misuse and exploitation of personal information. The bill aims to ensure companies are leveraged by consumer information for the development of AI without a transparent process of consent.

3. Transparent Consent Procedures: The bill calls for clear and conspicuous disclosures to be provided by the companies for the intended use of consumer data for AI training. The entities must provide a comprehensive explanation of data processing and its implications for consumers. The transparency fostered by the proposed bill allows consumers to make sound decisions about their data and its management, hence nurturing a sense of accountability and trust in data-driven practices.

4. Regulatory Compliance: The bill's guidelines call for strict requirements for procuring the consent of an individual. The entities must follow a prescribed mechanism for content solicitation, making the process streamlined and accessible for consumers. Moreover, the acquisition of content must be independent, i.e. without terms of service and other contractual obligations. These provisions underscore the importance of active and informed consent in data processing activities, reinforcing the principles of data protection and privacy.

5. Enforcement and Oversight: To enforce compliance with the provisions of the bill, robust mechanisms for oversight and enforcement are established. Violations of the prescribed regulations are treated as unfair or deceptive acts under its provisions. Empowering regulatory bodies like the FTC to ensure adherence to data privacy standards. By holding covered entities accountable for compliance, the bill fosters a culture of accountability and responsibility in data handling practices, thereby enhancing consumer trust and confidence in the digital ecosystem.

Importance of Data Anonymization

Data Anonymization is the process of concealing or removing personal or private information from the data set to safeguard the privacy of the individual associated with it. Anonymised data is a sort of information sanitisation in which data anonymisation techniques encrypt or delete personally identifying information from datasets to protect data privacy of the subject. This reduces the danger of unintentional exposure during information transfer across borders and allows for easier assessment and analytics after anonymisation. When personal information is compromised, the organisation suffers not just a security breach but also a breach of confidence from the client or consumer. Such assaults can result in a wide range of privacy infractions, including breach of contract, discrimination, and identity theft.

The AI consent bill asks the FTC to study data de-identification methods. Data anonymisation is critical to improving privacy protection since it reduces the danger of re-identification and unauthorised access to personal information. Regulatory bodies can increase privacy safeguards and reduce privacy risks connected with data processing operations by investigating and perhaps implementing anonymisation procedures.

The AI consent bill emphasises de-identification methods, as well as the DPDP Act 2023 in India, while not specifically talking about data de-identification, but it emphasises the data minimisation principles, which highlights the potential future focus on data anonymisation processes or techniques in India.

Conclusion

The proposed AI Consent bill in the US represents a significant step towards enhancing consumer privacy rights and data protection in the context of AI development. Through its stringent prohibitions, transparent consent procedures, regulatory compliance measures, and robust enforcement mechanisms, the bill strives to strike a balance between fostering innovation in AI technologies while safeguarding the privacy and autonomy of individuals.

References:

• https://fedscoop.com/consumer-data-consent-training-ai-models-senate-bill/#:~:text=%E2%80%9CThe%20AI%20CONSENT%20Act%20gives,Welch%20said%20in%20a%20statement
• https://www.dataguidance.com/news/usa-bill-ai-consent-act-introduced-house#:~:text=USA%3A%20Bill%20for%20the%20AI%20Consent%20Act%20introduced%20to%20House%20of%20Representatives,-ConsentPrivacy%20Law&text=On%20March%2019%2C%202024%2C%20US,the%20U.S.%20House%20of%20Representatives
• https://datenrecht.ch/en/usa-ai-consent-act-vorgeschlagen/
• https://www.lujan.senate.gov/newsroom/press-releases/lujan-welch-introduce-billto-require-online-platforms-receive-consumers-consent-before-using-their-personal-data-to-train-ai-models/

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Introduction

Language is an important part of human communication and a basic aspect of human understanding. The world is a global market and this diversity of languages has led to difficulties in engaging for effective communication and collaboration.  India alone has 22 official languages and countless regional languages and dialects which change every few hundred kilometres. 

AI has emerged to overcome this challenge of language barriers and has stepped into bringing about a transformative shift. It is leading the charge in breaking down traditional barriers and paving the way for more inclusive and seamless global interactions. AI’s integration into language translation has revolutionised the field, addressing longstanding challenges associated with traditional human-centric approaches. The limitations posed by reliance on human translators, such as time constraints, resource limitations, and the inability to handle the data efficiently, paved the way for the furtherance of the transformative impact of AI. However, challenges such as maintaining translation accuracy, addressing cultural nuances, and ensuring data privacy require careful attention to realize AI's full potential.

AI Technologies Bridging Language Gaps

AI tools have transformed translation, transcription, and natural language processing, providing language solutions. They can instantly translate text, transcribe audio, and analyse linguistic nuances, enabling effective cross-cultural communication. Moreover, AI's adaptive capabilities have facilitated language learning, allowing individuals to grasp new languages and adapt their communication styles to diverse cultural contexts.  

AI technologies are making information and services more accessible to non-native speakers and are impacting global business, allowing effective engagement. Building on this transformative potential, various AI tools are now used to bridge language gaps in real-world applications. Some examples of AI’s role in bridging the language gap are:

• Real-time translation tools that enable instant communication by providing translations between languages on the fly. This would help in effortless conversations with clients and partners worldwide. 
• Tools such as ‘speech-to-text’ and ‘text-to-speech’ like Murf AI, Lovo AI, and ElevenLabs work towards converting spoken language into written text and vice versa. These technologies have led to streamlined interactions, boosted productivity, and clarity in global business dealings. Businesses can extract important information, insights, and action points from meetings, interviews, and presentations. 
• AI chatbots like MyGov Corona Helpdesk, WhatsApp Chatbot by the Government of India, Railway Food Order & Delivery by Zoop India, and Gen AI-Powered 'Elena' by Indian School of Business (ISB) are some examples that act as intelligent virtual assistants that engage in real-time conversations, by answering queries, providing information, and facilitating transactions. They offer round-the-clock support, freeing human resources and enhancing customer experience across language barriers.

Challenges and Limitations of AI Translation

While AI’s integration in combatting language barriers is commendable, there are challenges and limitations in overcoming this endeavour. These challenges and limitations are:

• AI translation systems face several challenges in handling accuracy, context, nuance, and idiomatic expressions. 
• These systems may encounter struggles with complex or specialised language, along with those towards regional dialects, leading to potential misinterpretations. 
• Biases within the AI models can further affect the inclusivity of translations, often favouring dominant languages and cultural norms while marginalising others. 
• Ethical concerns, regarding privacy and data security, particularly when sensitive information is processed have also been arising. 
• Ensuring user consent and protecting data integrity are essential to addressing these concerns. As AI continues to evolve, ongoing efforts are needed to improve fairness, transparency, and the cultural sensitivity of translation systems.

AI’s Future  in Language Translation

AI technologies are moving towards improving translation accuracy and contextual understanding, allowing AI models to grasp cultural nuances and idiomatic expressions better. This can significantly enhance communication across diverse languages, fostering multilingual interactions and global collaboration in business, education, and diplomacy. Improvements in AI tech are taking place ubiquitous, and models like GPT and Google Translate are now better at capturing nuances, idioms, and cultural differences, reducing errors. AI tools like the Microsoft Translator help cross-continental teams work seamlessly by enhancing their productivity and inclusivity. 

AI is capable of offering real-time translation in healthcare, education, and public services. This would enable more inclusive environments and bridging communication gaps. For example in the healthcare system, AI-powered translation tools are helping the industry to provide better care by crossing linguistic barriers. Doctors can now communicate with patients who speak different languages, ensuring equitable care even with linguistic boundaries. 

Conclusion

We live in a world where diverse languages pose significant challenges to global communication, and AI has emerged as a powerful tool to bridge these gaps. AI is paving the way for more inclusive and seamless interactions by revolutionising language translation, transcription, and natural language processing. Its ability to break down barriers caused by linguistic diversity ensures effective communication in fields ranging from business to healthcare. Despite challenges like accuracy and cultural sensitivity, the potential for AI to continuously improve is undeniable. As AI technologies evolve, they stand as the key to overcoming language barriers and fostering a more connected and inclusive global community.

Notwithstanding AI's potential abilities to overcome language barriers through advances in natural language processing and translation, cybersecurity and data privacy must always come first. The same technologies that make it easier to communicate globally also put private information at risk. The likelihood of data breaches, personal information misuse, and compromised communication rises in the absence of strict cybersecurity safeguards. Thus, in order to guarantee safe and reliable international Interactions as AI develops, it is crucial to strike a balance between innovation and privacy protection.

References

• https://megasisnetwork.medium.com/ai-and-language-translation-breaking-down-language-barriers-47873cfdb13b
• https://pubmed.ncbi.nlm.nih.gov/38099504/ 
• https://www.linkedin.com/pulse/breaking-language-barriers-ai-era-leveraging-tools-business-a-rad
• https://www.researchgate.net/publication/373842132_Breaking_Down_Barriers_With_Artificial_Intelligence_AI_Cross-Cultural_Communication_in_Foreign_Language_Education

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