Can AI Detect Cheating in a Relationship?

Artificial intelligence (AI) is in nearly every aspect of our modern life, from optimising supply chains to personalising social media feeds and dating.

Its latest frontier is the deeply personal domain of romantic relationships, where the question of whether AI can detect infidelity has sparked growing interest.

With smartphones, social media, and wearable devices generating vast amounts of data, AI tools promise to uncover hidden patterns of behaviour—secretive texts, suspicious locations, or unusual interactions—that might suggest cheating.

Apps like mSpy and FlexiSPY already market themselves for monitoring digital activity, while social media analytics tools like Brandwatch analyse online behaviour.

Yet, the use of AI in such intimate contexts is marred by controversy and varying opinions, raising questions about privacy, trust, and the emotional nuances that AI technology struggles to comprehend, especially considering that AI is still in its early stages.

This article examines the technical capabilities of AI for detecting infidelity, assesses its reliability, and discusses the legal, ethical and social implications of deploying such tools in relationships.

Technical Approaches to AI-Based Cheating Detection

AI leverages advanced algorithms and data processing to identify potential signs of infidelity.

Below are the primary technical approaches grounded in real-world tools and methods.

1. Machine Learning & Behavioural Analytics
2. Digital Forensics & Biometric Tools

Machine Learning & Behavioural Analytics

Language and Text-Based Deception Signals

AI can analyse communication patterns using natural language processing (NLP) techniques. Tools like mSpy and FlexiSPY, which are installed on a target device, monitor text messages, emails, and app-based chats (e.g., WhatsApp, Signal). These apps use NLP to detect:

• Sentiment Shifts: A sudden change in tone, such as the use of overly formal or secretive language, may indicate hidden interactions. For example, mSpy can flag messages that use affectionate terms with unfamiliar contacts.
• Keyword Detection: Phrases like “don’t tell” or “meet me later” can be flagged for review. FlexiSPY’s keyword alerts notify users when specific words appear in their partners’ communications.
• Frequency and Context: AI can identify anomalies, such as a spike in late-night texts to a new contact. For instance, mSpy’s dashboard highlights unusual texting patterns based on time and recipient.

Pattern Recognition in Communication Metadata

Beyond message content, AI examines metadata—call logs, message frequency, and contact patterns.

Apps like Cocospy track call durations and frequency, flagging frequent calls to unknown numbers. Cocospy bills itself as a “mobile phone monitoring” (read: spyware) app for Android and iOS that allows you to peek into someone else’s device activity remotely.

Cocospy is marketed for parents worried about kids’ screen time or bosses keeping tabs on company phones, though “stealth mode” and hidden installs give it decidedly Stasi vibes.

You can legally monitor a minor child’s device or any phone you own, but sneaking spyware onto someone else’s phone without explicit consent is illegal in many jurisdictions. Always check local laws before playing Big Brother.

Social media monitoring tools, such as Brandwatch, analyse interactions on platforms like Instagram or X, detecting excessive likes, comments, or direct messages sent to a specific user.

For example, Brandwatch’s sentiment analysis can identify if a partner’s interactions with another person are unusually positive or frequent, suggesting a deeper connection. Brandwatch is not an official cheating detector, but it can detect sudden spikes in mentions, unusual sentiment shifts, or the emergence of new trends or hashtags related to your brand.

Digital Forensics & Biometric Tools

Voice Analysis and Deep-Fake Detection

AI-driven voice analysis tools, such as those developed by companies like Nemesysco, analyse vocal cues—such as pitch, hesitations, or stress markers—to detect potential deception. Nemesysco is a genuine emotion detection and risk assessment software that uses layered voice analysis.

While primarily used in security, these tools could theoretically flag evasive responses during conversations about a partner’s whereabouts.

Deep-fake detection, used by platforms like Deepware Scanner, can identify manipulated audio or video calls, ensuring that a partner’s claimed interactions (e.g., a video call with a “friend”) are authentic.

However, these technologies are not yet mainstream for personal use, but don’t worry, they will be soon enough.

Image and Video Forensics

AI can analyse photos or videos for signs of infidelity. Tools like FotoForensics use error-level analysis to detect edited images, such as a partner altering a photo to hide their location.

Social media platforms like Instagram provide metadata (e.g., geotags) that AI can cross-reference with a partner’s claimed whereabouts.

For example, a photo posted from a restaurant, claiming to be taken while at work, could raise suspicion.

Physiological Sensing

Wearable devices, such as Fitbit or Apple Watch, collect biometric data—heart rate, skin temperature, or motion—that AI can analyse for anomalies.

For instance, a sudden heart rate spike during a late-night call might suggest emotional arousal, though this is speculative and lacks precision.

Here are a few first-hand accounts from published articles where people’s Fitbit or Apple Watch data charted the moment their relationships ended:

“According to the data, Soto’s heart rate stayed relatively consistent from about midnight to noon. Once noon hit, however, it spiked — and spiked, and spiked and spiked.”

Sophie Kleeman, on Koby Soto’s Fitbit graph of his breakup

“Koby’s heart rate went from a normal 58 to 70-some beats per minute range to a more erratic and all over the map 65 to 116 beats per minute during his breakup (which went down around noon).”

Korin Miller, recounting the same story for Women’s Health

Apps like Cardiogram use AI to interpret wearable data, but their application to infidelity detection remains limited due to contextual ambiguity.

Reliability and Limitations

While AI’s capabilities are impressive, its reliability in detecting cheating is constrained by several factors.

Noise and False Positives in Behavioural and Biometric Data

AI often misinterprets innocent behaviour as suspicious. For example, mSpy might flag late-night texts to a coworker as infidelity, ignoring a work-related context.

Biometric data from wearables is particularly noisy; a heart rate spike could result from exercise or stress, not cheating.

False positives can erode trust, while false negatives—missing actual infidelity due to encrypted apps like Signal—limit effectiveness.

Challenges of Real-World Deployment vs. Controlled Experiments

In controlled settings, AI can achieve high accuracy; however, real-world relationships are often more complex and messy.

Apps like FlexiSPY require installation on a partner’s device, which may not capture all data if the partner uses a secondary phone.

Social media tools like Brandwatch rely on publicly available data and may overlook private messages or accounts that are not publicly accessible. The dynamic nature of relationships makes it hard for AI to establish a reliable baseline for “normal” behaviour.

Data Quality, Bias, and Interpretability Issues

The accuracy of AI depends on the quality of the data it is trained on. Incomplete data, such as partial access to a partner’s phone, leads to skewed results. Bias in algorithms can also misinterpret cultural or personal communication styles—e.g., flagging affectionate language common in some friendships as romantic.

Moreover, interpreting AI outputs requires human judgment, as tools like mSpy provide raw data (e.g., text logs) without definitive conclusions about infidelity.

Ethical and Privacy Considerations

Using AI to detect cheating raises profound ethical concerns that often outweigh its benefits and can lead to serious legal troubles.

Consent and Autonomy in Intimate Surveillance

Monitoring a partner’s phone or social media without consent, as enabled by apps like mSpy or Cocospy, violates personal autonomy. Even with consent, the power dynamic of one partner surveilling another can undermine trust.

For example, installing FlexiSPY without permission is illegal in many jurisdictions, including the United States and the European Union, as it violates applicable data protection and privacy laws.

Risks of Coercion, Abuse, and Power Imbalance

AI monitoring tools can be weaponised in abusive relationships. Apps like mSpy have been criticised for enabling coercive control, where one partner uses surveillance to manipulate or intimidate.

The availability of such tools on mainstream app stores amplifies these risks, as they are accessible to anyone with basic technical skills.

Data Ownership, Storage, and Security Concerns

AI tools collect sensitive data—such as texts, locations, and photos—that must be stored securely.

Apps like FlexiSPY store data on cloud servers, which raises the risk of hacks or leaks.

In 2017, mSpy suffered a data breach exposing user logs, highlighting the vulnerability of such systems. Users also lose control over their data once it’s shared with third-party apps, which may sell it to advertisers.

Legal and Social Implications

The use of AI in relationships extends beyond personal ethics to broader legal and social consequences.

Admissibility of AI-Derived Evidence in Legal Settings

Evidence from AI tools like mSpy is often inadmissible in court due to privacy violations.

For example, in the US, the Electronic Communications Privacy Act (ECPA) prohibits unauthorised interception of communications, rendering data from apps like FlexiSPY legally questionable.

At the continental level, the African Union Convention on Cyber Security and Personal Data Protection (the “Malabo Convention”) obliges signatories to enact “adequate legal frameworks” for lawful interception, data processing, and protection of personal data, stipulating that evidence collected outside these frameworks shall have no probative value in courts au.int.

Although ratification remains uneven, many African states are aligning their domestic laws—emphasising consent, judicial oversight, and data integrity—to mirror these principles, reinforcing that AI-derived surveillance evidence lacking proper legal authorisation is inadmissible across the continent.

Courts typically require explicit warrants or consent for the use of monitoring software or devices, which limits the utility of AI-derived evidence in divorce and custody cases.

Potential Shifts in Societal Norms Around Trust and Privacy

The widespread use of AI monitoring could normalise surveillance in relationships, eroding trust as a default expectation.

If tools like Cocospy become commonplace, partners may feel pressured to share data to “prove” fidelity, creating a culture of mistrust. However, it won’t be easy to see how these apps can become commonplace given the tight regulations in the EU, the US, and African countries.

This shift could also desensitise individuals to privacy invasions in other contexts, such as workplace monitoring.

Regulatory Landscape and Emerging Guidelines

In many countries, regulations often lag behind the rapid adoption of AI. In the EU, GDPR imposes strict rules on data collection, making non-consensual use of apps like mSpy illegal.

In the United States, laws vary by state, but unauthorised monitoring often violates wiretapping statutes. Emerging guidelines, such as those from the Federal Trade Commission (FTC), recommend that developers prioritise user consent and data security; however, enforcement remains inconsistent.

In South Africa, the Regulation of Interception of Communications and Provision of Communication-related Information Act (RICA) makes it a criminal offence to intercept any “communication”—voice, data or SMS—without a warrant issued by a designated judge; any evidence gathered outside RICA’s strict framework is generally excluded under the Criminal Procedure Act.

Complementing RICA, the Protection of Personal Information Act (POPIA) requires that personal data be processed lawfully, fairly and transparently. It empowers data subjects to demand the deletion of information that is “inaccurate, irrelevant, excessive, … or obtained unlawfully,” further undermining the admissibility of illicitly acquired data.

Conclusion

AI’s ability to detect cheating in relationships through tools like mSpy, FlexiSPY, and social media analytics platforms like Brandwatch is both powerful and limited.

These technologies can analyse texts, track locations, and monitor social media, flagging potential signs of infidelity with impressive precision in controlled settings.

However, their real-world reliability is hindered by false positives, incomplete data, and the inability to grasp emotional context fully.