Deepfake attacks in mobile banking: A growing threat to app security in 2025

Deepfakes are no longer limited to science fiction. What was once confined to viral videos and Hollywood studios has evolved into a tangible cybersecurity threat with profound implications for financial institutions. They’ve created challenges for mobile banking security—the kind we haven’t known earlier—and threaten the authentication systems that millions of us rely on for secure transactions.

As banks increasingly implement facial recognition to verify identity and authenticate transactions, fraudsters are using deepfake technology to bypass these systems. And just one successful spoof can lead to account takeover, identity theft, and major financial losses.

This post dissects modern face authentication, the critical vulnerability points where deepfakes can strike, and outlines how you can defend against them to stay secure.

How face authentication works in banking apps 

Face authentication in mobile banking begins when a user initiates authentication. While many assume that mobile apps cannot directly interface with camera hardware, operating system sandboxing creates barriers that prevent direct access to enhance security and create architectural complexity.

Each layer presents distinct attack surfaces that deepfake technologies can exploit. The flow of data—from physical camera through kernel drivers and system services to the application layer—creates multiple intervention points for attackers.

Banks usually deploy one of these two approaches:

1. Platform-level biometry services: They use Apple Face ID or Android's BiometricPrompt API to acquire, process, and match biometric data within the operating system.
2. In-app SDK-based FaceID systems: These solutions access video frames from the operating system's camera service and perform facial recognition using algorithms embedded within the app or processed on a server. While it is more customizable, this model increases the potential attack surface. 

How attackers target the authentication process

Threat actors’ difficulty increases as they move from application-level exploits to hardware manipulation. This ascending complexity creates a natural prioritization for both attackers and defenders.

App/SDK level manipulation (easiest)

At the application level, simple techniques like app repackaging or virtualization environments can compromise security. Tools like Meta Wolf create containerized environments where apps run with modified security contexts and allow for data interception and manipulation. Attackers also use hooking frameworks to intercept data flows within the app's process. These attacks require minimal technical sophistication but can be devastatingly effective against poorly protected apps.

System service/kernel-level manipulation (moderate)

System service attacks represent a middle ground of complexity. Specialized tools like VCAM and VCAMSX hook directly into your camera service layer and let attackers inject synthetic video streams that appear authentic to apps. These attacks require root access or privileged permissions, raising the technical bar a little higher.

Hardware-level attacks (most complex)

Hardware-level attacks on the camera feed are some of the most sophisticated threat vectors. While these attacks need technical expertise and physical access to the device, they can completely bypass software-based detection. An attacker could intercept the camera feed at the hardware level before it even reaches the OS kernel.

Platform differences: iOS vs Android

The security posture of face authentication varies between mobile platforms. Understanding these differences helps you develop an effective cross-platform security strategy that addresses the specific vulnerabilities of each ecosystem.

Apple's iOS ecosystem has its advantages with its integrated approach. Face ID uses specialized hardware (TrueDepth camera and Secure Enclave) to process biometric data in a cryptographically protected environment which banking apps can use without accessing raw biometric data themselves.

Android presents a more complex security landscape with its variety of versions and devices. Not all Android devices support native face authentication. This pushes developers towards third-party SDKs. Many Android devices lack specialized hardware for secure biometric processing, forcing banks to rely on software-based solutions. Its open nature also makes it easier for malicious actors to deploy rooting tools, hooking frameworks, and virtualization environments that compromise authentication security.

What security monitoring can and cannot do

App security and monitoring tools work within clear boundaries set by platform security models. Within the application layer, these tools can:

• Monitor for app integrity violations
• Detect known virtualization frameworks and hooking tools (e.g., LSposed)
• Identify suspicious behaviors in the app environment
• Implement runtime application self-protection techniques
• Flag unauthorized app modifications

Visibility diminishes when attackers operate at the system service or kernel level. At the system service or kernel levels, monitoring tools can only detect indirect evidence of tampering (like anomalous behaviors left behind by tools such as VCAM). These tools often fail to detect hardware-level attacks because they happen before the data reaches the operating system.

While app shielding plays an important role in detecting hooking tools, virtualization environments, and unauthorized changes, it operates within the boundaries of the application layer. This is why it cannot fully defend against attacks that manipulate video input at the system or hardware level.

How to protect your banking app from deepfake attacks

Securing your mobile banking app against deepfake threats needs both immediate and long-term initiatives. You need to deploy a layered security approach that addresses vulnerabilities across the entire authentication stack.

Short-term defensive measures

1. App shielding: This helps detect and block hooking frameworks, virtualization tools, and debuggers, and detects if your app is running in a rooted or jailbroken environment. It also monitors your app’s integrity to detect repackaging attacks. But app shielding has limited success against attacks that originate below the application layer, like synthetic video injection or hardware-level manipulation.
2. KYC and identity verification: Working with KYC and identity verification providers ensures their systems have anti-deepfake technologies, including liveness detection and media forensics. Being on the frontlines of identity onboarding makes them an important defense layer in securing your app data and its users.
3. Behavioral analysis: Analyzing user behavior lets you detect suspicious login activity. It monitors how users interact with your app and flags unusual or risky behavior. If a login attempt or transaction shows behavior that doesn't match the user's normal patterns (as learned by your app), the app can flag it for additional verification or block it entirely. This helps detect fraud even when credentials or devices are stolen.
4. Transaction authentication: Instead of relying only on login credentials, you can use a strong transaction authentication that combines biometrics with other factors. This is especially helpful for high-risk activities like fund transfers or adding a new payee. By verifying both the user and their action, you can prevent unauthorized transactions. 

Long-term strategic initiatives

1. Advanced liveness detection: You can choose identity verification software that detects liveness by analyzing subtle cues like eye blinks, head movements, texture, and light reflection. This helps ensure the video originates from a live person and not a recording or a synthetic image.
2. AI-based anomaly detection: Third-party identity verification SDKs often use AI to detect flag discrepancies like unnatural movements of the face or compression that’s usually found in deepfakes. You can implement AI-based detectors to strengthen your app’s ability to spot a fake video.
3. Platform-provided biometric APIs: Because each platform has its specific vulnerabilities, you can prioritize the use of platform-provided biometrics (like Face ID or BiometricPrompt) when available because they often carry hardware protection and OS-level security updates.
4. Regular security testing: When you conduct regular penetration testing focused specifically on face authentication bypass techniques, you improve your app’s chance of detecting emerging threats. This includes face morphing, deepfake injection, and camera stream tampering.
5. OS-level biometric security: Because mobile apps operate in a sandboxed environment with limited access to raw camera and sensor outputs, it is difficult to detect synthetic media independently. This is why the responsibility to spot fake media also extends to platform providers. You can collaborate with them to harden biometric APIs and build more secure camera pipelines.

Regulatory and privacy implications

Beyond the technical challenges, deepfake attacks introduce regulatory and compliance concerns. GDPR and other data security frameworks consider biometric data "special category data" that requires stringent protection. Compromised biometric authentication constitutes a serious breach with mandatory reporting requirements and potential penalties.

But this isn’t all.

The regulatory landscape becomes even more complex when you consider that face authentication tools must simultaneously comply with anti-money laundering (AML) and know your customer (KYC) requirements. Banks need to balance implementing security controls while maintaining regulatory compliance across multiple jurisdictions.

Regulations like the PSD3 in Europe mandate strong customer authentication (SCA). And your app must demonstrate that it can resist spoofing to qualify. Using platform-provided biometric APIs helps. When you rely on Apple Face ID or Android BiometricPrompt, you avoid directly handling raw biometric data and reduce your regulatory risk profile while enhancing security.

As regulators begin to understand the risks of deepfake identity fraud, pressure will increase on both app developers and upstream service providers. Your KYC provider will be expected to demonstrate it can block deepfake onboarding attempts, and you could expect more OS-level protections from platform providers, like secure and tamper-resistant camera feeds. Security in mobile banking is no longer a single-player job—it's a shared responsibility.

Layered defense against deepfakes

Deepfake attacks on mobile banking face authentication are a persistent threat that no single security control can address.

To stay protected, you need a clear understanding of your authentication system’s architecture and its vulnerable points. Layered protections that combine platform security features, app hardening, behavioral monitoring, and advanced detection techniques can help.

It is important to know where your defenses are strong—and where they’re not—to prioritize security investments in your authentication pipeline. This requires working closely with your KYC partners and mobile platform providers to step up with deeper system-level safeguards.

In mobile banking, security isn't just a feature. It’s the foundation of your customer’s trust in you with their money. And to stay ahead, you’ll need to evolve and adapt your defenses as fast as the threats do.