All posts by Ross Anderson

Bugs in our pockets?

In August, Apple announced a system to check all our iPhones for illegal images, then delayed its launch after widespread pushback. Yet some governments continue to press for just such a surveillance system, and the EU is due to announce a new child protection law at the start of December.

Now, in Bugs in our Pockets: The Risks of Client-Side Scanning, colleagues and I take a long hard look at the options for mass surveillance via software embedded in people’s devices, as opposed to the current practice of monitoring our communications. Client-side scanning, as the agencies’ new wet dream is called, has a range of possible missions. While Apple and the FBI talked about finding still images of sex abuse, the EU was talking last year about videos and text too, and of targeting terrorism once the argument had been won on child protection. It can also use a number of possible technologies; in addition to the perceptual hash functions in the Apple proposal, there’s talk of machine-learning models. And, as a leaked EU internal report made clear, the preferred outcome for governments may be a mix of client-side and server-side scanning.

In our report, we provide a detailed analysis of scanning capabilities at both the client and the server, the trade-offs between false positives and false negatives, and the side effects – such as the ways in which adding scanning systems to citizens’ devices will open them up to new types of attack.

We did not set out to praise Apple’s proposal, but we ended up concluding that it was probably about the best that could be done. Even so, it did not come close to providing a system that a rational person might consider trustworthy.

Even if the engineering on the phone were perfect, a scanner brings within the user’s trust perimeter all those involved in targeting it – in deciding which photos go on the naughty list, or how to train any machine-learning models that riffle through your texts or watch your videos. Even if it starts out trained on images of child abuse that all agree are illegal, it’s easy for both insiders and outsiders to manipulate images to create both false negatives and false positives. The more we look at the detail, the less attractive such a system becomes. The measures required to limit the obvious abuses so constrain the design space that you end up with something that could not be very effective as a policing tool; and if the European institutions were to mandate its use – and there have already been some legislative skirmishes – they would open up their citizens to quite a range of avoidable harms. And that’s before you stop to remember that the European Court of Justice struck down the Data Retention Directive on the grounds that such bulk surveillance, without warrant or suspicion, was a grossly disproportionate infringement on privacy, even in the fight against terrorism. A client-side scanning mandate would invite the same fate.

But ‘if you build it, they will come’. If device vendors are compelled to install remote surveillance, the demands will start to roll in. Who could possibly be so cold-hearted as to argue against the system being extended to search for missing children? Then President Xi will want to know who has photos of the Dalai Lama, or of men standing in front of tanks; and copyright lawyers will get court orders blocking whatever they claim infringes their clients’ rights. Our phones, which have grown into extensions of our intimate private space, will be ours no more; they will be private no more; and we will all be less secure.

Is Apple’s NeuralMatch searching for abuse, or for people?

Apple stunned the tech industry on Thursday by announcing that the next version of iOS and macOS will contain a neural network to scan photos for sex abuse. Each photo will get an encrypted ‘safety voucher’ saying whether or not it’s suspect, and if more than about ten suspect photos are backed up to iCloud, then a clever cryptographic scheme will unlock the keys used to encrypt them. Apple staff or contractors can then look at the suspect photos and report them.

We’re told that the neural network was trained on 200,000 images of child sex abuse provided by the US National Center for Missing and Exploited Children. Neural networks are good at spotting images “similar” to those in their training set, and people unfamiliar with machine learning may assume that Apple’s network will recognise criminal acts. The police might even be happy if it recognises a sofa on which a number of acts took place. (You might be less happy, if you own a similar sofa.) Then again, it might learn to recognise naked children, and flag up a snap of your three-year-old child on the beach. So what the new software in your iPhone actually recognises is really important.

Now the neural network described in Apple’s documentation appears very similar to the networks used in face recognition (hat tip to Nicko van Someren for spotting this). So it seems a fair bet that the new software will recognise people whose faces appear in the abuse dataset on which it was trained.

So what will happen when someone’s iPhone flags ten pictures as suspect, and the Apple contractor who looks at them sees an adult with their clothes on? There’s a real chance that they’re either a criminal or a witness, so they’ll have to be reported to the police. In the case of a survivor who was victimised ten or twenty years ago, and whose pictures still circulate in the underground, this could mean traumatic secondary victimisation. It might even be their twin sibling, or a genuine false positive in the form of someone who just looks very much like them. What processes will Apple use to manage this? Not all US police forces are known for their sensitivity, particularly towards minority suspects.

But that’s just the beginning. Apple’s algorithm, NeuralMatch, stores a fingerprint of each image in its training set as a short string called a NeuralHash, so new pictures can easily be added to the list. Once the tech is built into your iPhone, your MacBook and your Apple Watch, and can scan billions of photos a day, there will be pressure to use it for other purposes. The other part of NCMEC’s mission is missing children. Can Apple resist demands to help find runaways? Could Tim Cook possibly be so cold-hearted as to refuse at add Madeleine McCann to the watch list?

After that, your guess is as good as mine. Depending on where you are, you might find your photos scanned for dissidents, religious leaders or the FBI’s most wanted. It also reminds me of the Rasterfahndung in 1970s Germany – the dragnet search of all digital data in the country for clues to the Baader-Meinhof gang. Only now it can be done at scale, and not just for the most serious crimes either.

Finally, there’s adversarial machine learning. Neural networks are fairly easy to fool in that an adversary can tweak images so they’re misclassified. Expect to see pictures of cats (and of Tim Cook) that get flagged as abuse, and gangs finding ways to get real abuse past the system. Apple’s new tech may end up being a distributed person-search machine, rather than a sex-abuse prevention machine.

Such a technology requires public scrutiny, and as the possession of child sex abuse images is a strict-liability offence, academics cannot work with them. While the crooks will dig out NeuralMatch from their devices and play with it, we cannot. It is possible in theory for Apple to get NeuralMatch to ignore faces; for example, it could blur all the faces in the training data, as Google does for photos in Street View. But they haven’t claimed they did that, and if they did, how could we check? Apple should therefore publish full details of NeuralMatch plus a set of NeuralHash values trained on a public dataset with which we can legally work. It also needs to explain how the system it deploys was tuned and tested; and how dragnet searches of people’s photo libraries will be restricted to those conducted by court order so that they are proportionate, necessary and in accordance with the law. If that cannot be done, the technology must be abandoned.

WEIS 2021 – Liveblog

I’ll be trying to liveblog the twentieth Workshop on the Economics of Information Security (WEIS), which is being held online today and tomorrow (June 28/29). The event was introduced by the co-chairs Dann Arce and Tyler Moore. 38 papers were submitted, and 15 accepted. My summaries of the sessions of accepted papers will appear as followups to this post; there will also be a panel session on the 29th, followed by a rump session for late-breaking results. (Added later: videos of the sessions are linked from the start of the followups that describe them.)

Cybercrime gangs as tech startups

In our latest paper, we propose a better way of analysing cybercrime.

Crime has been moving online, like everything else, for the past 25 years, and for the past decade or so it’s accounted for more than half of all property crimes in developed countries. Criminologists have tried to apply their traditional tools and methods to measure and understand it, yet even when these research teams include technologists, it always seems that there’s something missing. The people who phish your bank credentials are just not the same people who used to burgle your house. They have different backgrounds, different skills and different organisation.

We believe a missing factor is entrepreneurship. Cyber-crooks are running tech startups, and face the same problems as other tech entrepreneurs. There are preconditions that create the opportunity. There are barriers to entry to be overcome. There are pathways to scaling up, and bottlenecks that inhibit scaling. There are competitive factors, whether competing crooks or motivated defenders. And finally there may be saturation mechanisms that inhibit growth.

One difference with regular entrepreneurship is the lack of finance: a malware gang can’t raise VC to develop a cool new idea, or cash out by means on an IPO. They have to use their profits not just to pay themselves, but also to invest in new products and services. In effect, cybercrooks are trying to run a tech startup with the financial infrastructure of an ice-cream stall.

We have developed this framework from years of experience dealing with many types of cybercrime, and it appears to prove a useful way of analysing new scams, so we can spot those developments which, like ransomware, are capable of growing into a real problem.

Our paper Silicon Den: Cybercrime is Entrepreneurship will appear at WEIS on Monday.

Security engineering and machine learning

Last week I gave my first lecture in Edinburgh since becoming a professor there in February. It was also the first talk I’ve given in person to a live audience since February 2020.

My topic was the interaction between security engineering and machine learning. Many of the things that go wrong with machine-learning systems were already familiar in principle, as we’ve been using Bayesian techniques in spam filters and fraud engines for almost twenty years. Indeed, I warned about the risks of not being able to explain and justify the decisions of neural networks in the second edition of my book, back in 2008.

However the deep neural network (DNN) revolution since 2012 has drawn in hundreds of thousands of engineers, most of them without this background. Many fielded systems are extremely easy to break, often using tricks that have been around for years. What’s more, new attacks specific to DNNs – adversarial samples – have been found to exist for pretty well all models. They’re easy to find, and often transferable from one model to another.

I describe a number of new attacks and defences that we’ve discovered in the past three years, including the Taboo Trap, sponge attacks, data ordering attacks and markpainting. I argue that we will usually have to think of defences at the system level, rather than at the level of individual components; and that situational awareness is likely to play an important role.

Here now is the video of my talk.

A new way to detect ‘deepfake’ picture editing

Common graphics software now offers powerful tools for inpainting – using machine-learning models to reconstruct missing pieces of an image. They are widely used for picture editing and retouching, but like many sophisticated tools they can also be abused. They can remove someone from a picture of a crime scene, or remove a watermark from a stock photo. Could we make such abuses more difficult?

We introduce Markpainting, which uses adversarial machine-learning techniques to fool the inpainter into making its edits evident to the naked eye. An image owner can modify their image in subtle ways which are not themselves very visible, but will sabotage any attempt to inpaint it by adding visible information determined in advance by the markpainter.

One application is tamper-resistant marks. For example, a photo agency that makes stock photos available on its website with copyright watermarks can markpaint them in such a way that anyone using common editing software to remove a watermark will fail; the copyright mark will be markpainted right back. So watermarks can be made a lot more robust.

In the fight against fake news, markpainting news photos would mean that anyone trying to manipulate them would risk visible artefacts. So bad actors would have to check and retouch photos manually, rather than trying use inpainting tools to automate forgery at scale.

This paper has been accepted at ICML.

Patient confidentiality in remote consultations

During the lockdown last year, I was asked by the International Psychoanalytic Association (IPA) to help them update their guidance on remote consultations. I spoke to a range of GPs, surgeons, psychologists and psychoanalysts about what they’d learned during the first lockdown about working over the phone, or over Skype or Zoom. The IPA has now published my report, on a web page that also has their guidance to members both before and after the exercise.

Before the pandemic, remote consultation did happen, but not all therapists offered it; and confidentiality concerns tended to focus on technical security measures such as whether the call was encrypted end-to-end. After everyone was forced online in March and April 2020, clinicians learned rapidly to focus on the endpoints. Patients often have problems finding a private space to talk; there may be a family member in earshot, whether by accident, or because they’re cooped up in a tiny apartment, or because they have a controlling partner or parent. A clinician may return a patient’s call and catch them in a supermarket queue. And the clinic too can be interrupted, if the clinician is practicing from home.

Technical endpoint compromise is occasionally an issue; a controlling family member could inspect a patient’s device and discover a therapeutic relationship that had not been disclosed. By far the worst endpoint compromise that happened during the study period was when the Vastaamo chain of clinics in Finland was hit by ransomware; 45,000 patients’ records were stolen, and some were put online by extortionists demanding bitcoin payments. (And now we face an even larger-scale issue in the UK as the government plans to hoover up all our GP records for sale to drug companies unless we opt out by June 25; see here for how to do that.)

Such horrors aside, the core problem is to establish a therapeutic space where both patient and clinician can interact effectively, which means being able to concentrate and also to relax. There’s more to this than just being comfortable trusting the endpoint environments, the devices, the communications medium and any record-keeping mechanism. Interaction matters too. Many clinician communities discovered independently that the plain old telephone system often works better than new-fangled stuff such as skype and zoom. Video calls add maybe half a second of latency for buffering, which destroys conversational turn-taking. A further advantage of the phone is that you’re not staring at someone’s face at an unnatural distance. You can walk around the room, or even walk around the park.

Since doing this work I’ve started to avoid zoom and teams in favour of phone calls when I can, and use end-to-end encrypted voice calls on WhatsApp or Signal where call costs or client confidentiality make it sensible.

Robots, manners and stress

Humans and other animals have evolved to be aware of whether we’re under threat. When we’re on safe territory with family and friends we relax, but when we sense that a rival or a predator might be nearby, our fight-or-flight response kicks in. Situational awareness is vital, as it’s just too stressful to be alert all the time.

We’ve started to realise that this is likely to be just as important in many machine-learning applications. Take as an example machine vision in an automatic driver assistance system, whose goal is automatic lane keeping and automatic emergency braking. Such systems use deep neural networks, as they perform way better than the alternatives; but they can be easily fooled by adversarial examples. Should we worry? Sure, a bad person might cause a car crash by projecting a misleading image on a motorway bridge – but they could as easily steal some traffic cones from the road works. Nobody sits up at night worrying about that. But the car industry does actually detune vision systems from fear of deceptive attacks!

We therefore started a thread of research aimed at helping machine-learning systems detect whether they’re under attack. Our first idea was the Taboo Trap. You raise your kids to observe social taboos – to behave well and speak properly – and yet once you send them to school they suddenly know words that would make your granny blush. The taboo violation shows they’ve been exposed to ‘adversarial inputs’, as an ML engineer would call them. So we worked out how to train a neural network to avoid certain taboo values, both of outputs (forbidden utterances) and intermediate activations (forbidden thoughts). The taboos can be changed every time you retrain the network, giving the equivalent of a cryptographic key. Thus even though adversarial samples will always exist, you can make them harder to find; an attacker can’t just find one that works against one model of car and use it against every other model. You can take a view, based on risk, of how many different keys you need.

We then showed how you can also attack the availability of neural networks using sponge examples – inputs designed to soak up as much energy, and waste as much time, as possible. An alarm can be simpler to build in this case: just monitor how long your classifier takes to run.

Are there broader lessons? We suspect so. As robots develop situational awareness, like humans, and react to real or potential attacks by falling back to a more cautious mode of operation, a hostile environment will cause the equivalent of stress. Sometimes this will be deliberate; one can imagine constant low-level engagement between drones at tense national borders, just as countries currently probe each others’ air defences. But much of the time it may well be a by-product of poor automation design coupled with companies hustling aggressively for consumers’ attention.

This suggests a missing factor in machine-learning research: manners. We’ve evolved manners to signal to others that our intent is not hostile, and to negotiate the many little transactions that in a hostile environment might lead to a tussle for dominance. Yet these are hard for robots. Food-delivery robots can become unpopular for obstructing and harassing other pavement users; and one of the show-stoppers for automated driving is the difficulty that self-driving cars have in crossing traffic, or otherwise negotiating precedence with other road users. And even in the military, manners have a role – from the chivalry codes of medieval knights to the more modern protocols whereby warships and warplanes warn other craft before opening fire. If we let loose swarms of killer drones with no manners, conflict will be more likely.

Our paper Situational Awareness and Machine Learning – Robots, Manners and Stress was invited as a keynote for two co-located events: IEEE CogSIMA and the NATO STO SCI-341 Research Symposium on Situation awareness of Swarms and Autonomous systems. We got so many conflicting demands from the IEEE that we gave up on making a video of the talk for them, and our paper was pulled from their proceedings. However we decided to put the paper online for the benefit of the NATO folks, who were blameless in this matter.

Data ordering attacks

Most deep neural networks are trained by stochastic gradient descent. Now “stochastic” is a fancy Greek word for “random”; it means that the training data are fed into the model in random order.

So what happens if the bad guys can cause the order to be not random? You guessed it – all bets are off. Suppose for example a company or a country wanted to have a credit-scoring system that’s secretly sexist, but still be able to pretend that its training was actually fair. Well, they could assemble a set of financial data that was representative of the whole population, but start the model’s training on ten rich men and ten poor women drawn from that set – then let initialisation bias do the rest of the work.

Does this generalise? Indeed it does. Previously, people had assumed that in order to poison a model or introduce backdoors, you needed to add adversarial samples to the training data. Our latest paper shows that’s not necessary at all. If an adversary can manipulate the order in which batches of training data are presented to the model, they can undermine both its integrity (by poisoning it) and its availability (by causing training to be less effective, or take longer). This is quite general across models that use stochastic gradient descent.

This work helps remind us that computer systems with DNN components are still computer systems, and vulnerable to a wide range of well-known attacks. A lesson that cryptographers have learned repeatedly in the past is that if you rely on random numbers, they had better actually be random (remember preplay attacks) and you’d better not let an adversary anywhere near the pipeline that generates them (remember injection attacks). It’s time for the machine-learning community to carefully examine their assumptions about randomness.